As part of the team, you will help solve complex business problems by developing viable cutting-edge AI/ML solutions.

You will develop and implement creative solutions that fundamentally transform business processes, delivering breakthrough improvements rather than incremental changes.

You will work closely with other AI/ML researchers and engineers, SWEs, product owners/managers, and business stakeholders, and participate in the full lifecycle of solution development, including requirements gathering with business, experimentation and algorithmic exploration, development, and assistance with productization.

Key Responsibilities:

• Work independently or as part of a team to help design and implement high accuracy and delightful user experience solutions utilizing ML, NLP, GenAI, Agentic technologies.

• Participate in all aspects of solution development, including ideation and requirement gathering with business stakeholders, experimentation and exploration to identify strong solution approaches, solution development, etc.

• Prototype, test, and iterate on novel AI models and approaches to solve complex business challenges.

• Collaborate with cross-functional teams to identify opportunities where AI can create significant business value, and transition solutions into production systems.

• Research and stay updated with the latest advancements in machine learning and AI technologies.

• Participate in code reviews, technical discussions, and knowledge sharing sessions.

• Communicate technical concepts and transformative ideas effectively to both technical and non-technical stakeholders.

Required Skills & Qualifications:

• Bachelor's with 10+ years, Master's with 7+ years, or PhD with 5+ years in Computer Science, Data Science, Machine Learning, or related field.

• Deep expertise and proven ability in developing high accuracy/value solutions to business problems in the NLP, Generative AI, Agentic AI, and/or ML space.

• Hands-on experience with data processing, experimentation, and exploration.

• Strong programming skills in Python.

• Experience with cloud platforms (AWS, Azure, GCP) for deploying ML solutions.

• Excellent problem-solving skills and attention to detail.

• Strong communication skills to collaborate with technical and non-technical stakeholders.

• Ability to work independently and collaboratively.

Additional Preferred Skills & Qualifications:

• Understanding of the financial markets, including experience with financial datasets, is strongly preferred.

• Experience with ML frameworks such as PyTorch, TensorFlow.

• Familiarity with MLOps practices and tools such as SageMaker, MLflow, or Airflow.

• Previous experience working in an Agile environment.

Millennium pays a total compensation package which includes a base salary, discretionary performance bonus, and a comprehensive benefits package. The estimated base salary range for this position is $175,000 to $250,000, which is specific to New York and may change in the future.

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The successful candidate will be expected to:

• Help design and contribute to the alpha research platform
• Support, maintain, and test their own code following best practices, including unit testing, regression testing, documentation, and automation within typical CI processes
• Provide leadership and vision to help determine the overall direction, design, and architecture of the alpha research pipeline
• Mentor junior resources
• Regularly interact with quantitative researchers and other stakeholders, and prioritise and implement features

The ideal candidate will have:

• 5+ years of Python experience in a quantitative finance setting
• Familiarity with linear models and basic statistics for creating model evaluation and reporting workflows
• Familiarity with the Python data science ecosystem, including dashboarding and popular ML libraries such as Plotly, Altair, JAX, TensorFlow, and PyTorch
• Prior experience building alpha research or machine learning pipelines
• Highly analytical with strong problem-solving skills and attention to detail
• Strong communication skills, with the ability to explain technical and sophisticated concepts clearly and concisely
• Ability to tune and debug runtime performance of data applications
• Familiarity with C++/Rust/CUDA to debug and profile underlying native code in ML libraries (Nice to have)

The estimated base salary range for this position is $160,000 to $250,000, which is specific to New York and may change in the future.

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Your tasks will include:

• Working with a team of product managers, agronomists, and developers to integrate and adapt models into existing applications
• Conducting data analyses and visualizing results
• Supporting the development of innovative solutions for precision agriculture

We offer a dynamic and inclusive work environment where you can bring your ideas and perspectives to the table. Our team is committed to making a positive impact on the world through our work.

As a practicum student, you will have the opportunity to gain hands-on experience and develop new skills in a real-world setting. You will be supported by experienced colleagues and have access to a range of resources and training opportunities.

If you are passionate about AI, data science, and precision agriculture, we encourage you to apply for this exciting opportunity.

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The primary responsibilities of this role are to:

Solve real agricultural problems using deep learning and AI across image and other data modalities, translating complex models into tangible business and scientific impact.

Design and implement end-to-end machine learning pipelines for computer vision use cases, including segmentation, classification, detection, and multi-task learning.

Prototype, evaluate, and iterate on cutting-edge architectures such as CNNs, Vision Transformers, foundational and large-scale vision models, ensuring state-of-the-art performance.

Optimize models for accuracy, robustness, and inference efficiency, including experimentation with hyperparameters, compression, and deployment-oriented optimisations.

Independently build scalable data pipelines for training, validation, and evaluation, including data ingestion, augmentation strategies, and active learning loops.

Collaborate cross-functionally with product, data, and software engineering teams to integrate models into production systems and deliver reliable, maintainable solutions.

Contribute to MLOps practices, including model versioning, deployment, monitoring, and retraining workflows using modern tooling and cloud-based platforms.

Build strong cross-functional relationships and actively engage with the broader Data Science Community to share best practices, align on standards, and co-create innovative solutions.

Present clear, compelling, and validated stories about experiments, results, and recommendations to peers, senior management, and internal customers to drive strategic and operational decisions.

We seek an incumbent who possesses the following:

M.S. with 2+ years of experience or Ph.D. in Computer Science, Electrical Engineering, or a related field with a focus on machine learning or computer vision.

Proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow.

Hands-on experience with modern computer vision architectures including models such as ResNet, UNet, DeepLab, YOLO, SegFormer, SAM, and Vision Transformers.

Strong background in handling large-scale datasets and creating custom datasets, for example using frameworks such as Hugging Face Datasets.

Solid understanding of core machine learning concepts including loss functions, regularization, optimisation, and learning rate scheduling.

Experience developing and deploying models using cloud-based ML platforms such as AWS SageMaker.

Familiarity with Unix environments, including bash, file systems, and core utilities.

Strong engineering practices including use of Git, Docker, CI/CD pipelines, modular codebase design, and unit testing.

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As a hands-on contributor to the core systems that ensure the safety, reliability, and continuous improvement of LLM-powered workflows and agents for the enterprise, you will partner with Scale's Operations team and enterprise customers to translate ambiguity into structured evaluation data. This involves guiding the creation and maintenance of gold-standard human-rated datasets and expert rubrics that anchor AI evaluation systems.

Your responsibilities will also include analysing feedback and collected data to identify patterns, refine evaluation frameworks, and establish iterative improvement loops that enhance the quality and relevance of human-curated assessments. You will design, research, and develop LLM-as-a-Judge autorater frameworks and AI-assisted evaluation systems, including creating models that critique, grade, and explain agent outputs.

To succeed in this role, you will need a strong foundational knowledge of large language models, a passion for tackling complex evaluation challenges, and the ability to thrive in a dynamic, fast-paced research environment. You should be able to think outside the box, stay current with the latest literature in AI evaluation, and be passionate about integrating novel research ideas into our workflows to build best-in-class evaluation systems.

In addition to your technical expertise, you will need excellent communication and collaboration skills, as you will work closely with cross-functional teams to drive project success.

If you are a motivated and detail-oriented individual with a passion for AI research and evaluation, we encourage you to apply for this exciting opportunity.

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As an ML Engineer, you will design, implement, and scale automated evaluation pipelines that help customers trust and operationalize advanced AI systems across defense, intelligence, and federal missions.

Key responsibilities include:

• Developing and maintaining automated evaluation pipelines for ML models across functional, performance, robustness, and safety metrics, including LLM-judge–based evaluations.

• Designing test datasets and benchmarks to measure generalization, bias, explainability, and failure modes.

• Building evaluation frameworks for LLM agents, including infrastructure for scenario-based and environment-based testing.

• Conducting comparative analyses of model architectures, training procedures, and evaluation outcomes.

• Implementing tools for continuous monitoring, regression testing, and quality assurance for ML systems.

• Designing and executing stress tests and red-teaming workflows to uncover vulnerabilities and edge cases.

• Collaborating with operations teams and subject matter experts to produce high-quality evaluation datasets.

This role requires an active security clearance or the ability to obtain a security clearance.

Ideal candidates will have experience in computer vision, deep learning, reinforcement learning, or NLP in production settings, strong programming skills in Python, and background in algorithms, data structures, and object-oriented programming.

Nice to have qualifications include graduate degree in CS, ML, or AI, cloud experience (AWS, GCP), and model deployment experience.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training.

Scale employees in eligible roles are also granted equity-based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant.

You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO.

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As an HFC Fellow, you'll apply your academic and professional expertise to help design, evaluate, and interpret advanced generative AI systems.

Key responsibilities include:

• Engaging in high-impact projects with partnered AI labs and platforms
• Designing, reviewing, and optimising PyTorch models
• Evaluating complex ML code and AI-generated implementations for efficiency and correctness
• Advising on GPU optimisation, scaling, and trade-offs

You'll also become part of a supportive, interdisciplinary network of innovators and thought leaders committed to advancing frontier AI across domains.

Collaboration with Scale's research team to co-author technical reports and research papers is also expected.

To be eligible, candidates must be authorised to work in Canada and have a PhD or postdoctoral degree in Computer Science, Computer Engineering, or a related field.

Professional background as a Machine Learning Engineer or Data Scientist with 1-3+ years of experience is also required.

Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow) is essential, along with experience with cloud infrastructure (AWS) and MLOps tools (Docker, Langchain).

A detail-oriented, innovative thinker with a passion in applied AI research and a commitment to collaboration is ideal.

Flexible schedule with 10–40 hour weeks that fit around your life and other commitments is offered.

Project pay rates vary across platforms and are depending on a number of factors, including but not limited to; projects, scope, skillset, and location.

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At Scale AI, our mission is to accelerate the development of AI applications. This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents.

Responsibilities

• Explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation.
• Contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate these advancements into real-world, scalable solutions.

Requirements

• Practical experience working with LLMs, with proficiency in frameworks like Pytorch, Jax, or Tensorflow.
• A track record of published research in top ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, COLM, etc.).
• At least three years of experience addressing sophisticated ML problems, either in a research setting or product development.

Nice to Have

• Hands-on experience with open source LLM fine-tuning or involvement in bespoke LLM fine-tuning projects using Pytorch/Jax.
• Hands-on experience and publications in building applications and evaluations related to AI agents such as tool-use, text2SQL, browser agents, coding agents and GUI agents.
• Hands-on experience with agent frameworks such as OpenHands, Swarm, LangGraph, etc.
• Familiarity with agentic reasoning methods such as STaR and PLANSEARCH
• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment.

Benefits

• Comprehensive health, dental and vision coverage
• Retirement benefits
• A learning and development stipend
• Generous PTO
• Commuter stipend

Salary Range

$259,200-$324,000 USD

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At Scale, our mission is to accelerate the development of AI applications. We're looking for a Machine Learning Research Scientist/Engineer to join our team and help us shape the future of AI.

This role operates at the forefront of AI research and real-world implementation, with a strong focus on reasoning within large language models (LLMs). You will study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents. You will play a key role in shaping Scale's data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning.

Success in this role requires a deep understanding of LLMs, planning algorithms, and novel approaches to agentic reasoning, as well as creativity in tackling challenges related to data generation, model interaction, and evaluation. You will contribute to impactful research on language model reasoning, collaborate with external researchers, and work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions.

Responsibilities

• Study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents
• Shape Scale's data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning
• Contribute to impactful research on language model reasoning
• Collaborate with external researchers
• Work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions

Requirements

• Practical experience working with LLMs, with proficiency in frameworks like PyTorch, JAX, or TensorFlow
• A track record of published research in top ML and NLP venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, CoLLM, etc.)
• At least three years of experience solving complex ML challenges, either in a research setting or product development, particularly in areas related to LLM capabilities and reasoning
• Strong written and verbal communication skills, along with the ability to work effectively across teams

Nice to Have

• Hands-on experience fine-tuning open-source LLMs or leading bespoke LLM fine-tuning projects using PyTorch/JAX
• Research and practical experience in building applications and evaluations related to LLM-based agents, including tool-use, text-to-SQL, browser agents, coding agents, and GUI agents
• Experience with agent frameworks such as OpenHands, Swarm, LangGraph, or similar
• Familiarity with advanced agentic reasoning techniques such as STaR and PLANSEARCH
• Proficiency in cloud-based ML development, with experience in AWS or GCP environments

Benefits

• Comprehensive health, dental and vision coverage
• Retirement benefits
• A learning and development stipend
• Generous PTO
• Commuter stipend

Salary Range

$252,000-$315,000 USD

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Lead applied ML engineering on Scale's Applied ML team, powering data infrastructure for leading agentic LLMs (ChatGPT, Gemini, Llama). You will build scalable multi-agent systems to validate agentic reasoning and behaviours, scale human expertise, and drive research into real-world agent reliability failures despite strong benchmarks, shipping production fixes.

Responsibilities

• Build and deploy multi-agent systems for agentic reasoning validation
• Develop pipelines to detect errors and scale human judgment
• Combine classical ML, LLMs, and multi-agent techniques for reliability
• Lead research into agent failure modes and ship fixes
• Use AI tools to speed prototyping and iteration
• Build data-driven evaluations and deploy rapid improvements
• Integrate systems into Scale's platform

Ideal Candidate

• PhD or MSc in Computer Science, Mathematics, Statistics, or related field
• 3+ years shipping scaled production ML systems
• Demonstrated real-world impact
• Mastery of PyTorch, TensorFlow, JAX, or scikit-learn
• Deep expertise in agentic LLMs and multi-agent systems
• Strong software engineering and microservices (AWS/GCP)
• Rapid, data-driven iteration
• Proficiency using AI tools to accelerate work
• Strong research depth with practical bias
• Excellent cross-functional communication

Nice to Have

• Experience prototyping agent evaluation/reliability systems
• Human-in-the-loop or annotation pipeline work
• Open-source contributions in agents, evaluation, or alignment
• Publications on agent reliability (NeurIPS, ICML, ICLR)

Compensation

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity-based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

About Us

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

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Taniam is seeking an AI Research Scientist Intern for Summer 2026. This role reports to our Staff Data Scientist leading AI design, prototyping, and experimentation across Tanium products and solutions.

As an AI Research Scientist Intern, you will have the opportunity to work on challenging real-world problems, gain hands-on experience, and contribute to building intelligent systems that will shape the future of autonomous IT across endpoint management, exposure management, and security operations.

This is a hybrid position, which will require in person attendance several days each week in Durham, NC OR Emeryville, CA.

The hourly rate for this internship is $55 to $60 per hour. This hourly rate is an estimate for what Tanium will pay an intern. The actual rate offered may be adjusted based on a variety of factors, including but not limited to, education, skills, training, and experience.

In addition to an hourly rate, interns will be eligible for a housing stipend, 401k matching, and a monthly allowance for communications reimbursement.

Responsibilities

• Collaborate with data science and engineering teams to design and implement AI and machine learning (ML) algorithms that improve Tanium’s autonomous IT capabilities.

• Develop and evaluate ML models across a wide variety of use cases, including anomaly detection, threat identification, and performance optimization.

• Analyze large-scale endpoint data to extract meaningful insights and develop predictive models.

• Assist in building data pipelines to efficiently process and analyze endpoint data at scale.

• Conduct exploratory data analyses and visualizations to communicate findings and inform product development decisions.

• Document research, methodologies, and results to contribute to Tanium’s knowledge base.

• Stay up to date with the latest advancements in AI, ML, and endpoint management technologies.

Requirements

• Strong foundation in machine learning, data analysis, and statistics.

• Proficiency in Python.

• Familiarity with machine learning frameworks and libraries (e.g., PyTorch, scikit-learn, TensorFlow, numpy, scipy, pandas).

• Experience working with large datasets.

• Knowledge in one or more of the following areas:

• Statistics

• Anomaly detection

• Machine learning (classification/regression)

• Internals of large language models (LLMs)

• Agentic AI

• Strong problem-solving skills and the ability to work independently and collaboratively.

• Excellent communication skills and the ability to present complex ideas clearly.

• Passion for technology, cybersecurity, and autonomous systems.

Other qualifications:

• Authorized to work in the U.S. now and in the future

• Available to work full-time from June 8, 2024 to August 14, 2025

• A currently enrolled Master’s/ PhD degree candidate ideally studying Statistics, Machine Learning, or a related field with a 3.5+ GPA

• Graduating Spring 2027 or Fall 2026

• In accordance with Department of Defense requirements, applicants for this role must be a U.S. citizen, national, or resident pursuant to 8 U.S.C. 1101(a)(20) and 8 U.S.C. 1324b(a)(3)

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Our team works on high-impact systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes. You'll work on complex, real-world ML problems at massive scale, and contribute to technical strategy, architecture, and long-term ML roadmap.

Responsibilities:

• Design, build, and deploy production-grade machine learning models and systems at scale
• Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring
• Build scalable data and model pipelines with strong reliability, observability, and automated retraining
• Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems
• Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions
• Improve system performance across latency, throughput, and model quality metrics
• Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph & transformers based, and LLM evaluation/alignment

Basic Qualifications:

• 3-5+ years of experience building, deploying, and operating machine learning systems in production
• Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals
• ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)
• Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
• Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure
• Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions
• Experience improving measurable metrics through applied machine learning

Preferred Qualifications:

• Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems
• Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)
• Experience working with real-time systems and low-latency production environments
• Background in feature engineering, model optimization, and production monitoring
• Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale
• Advanced degree in Computer Science, Machine Learning, or related quantitative field

Potential Teams:

• Ads Measurement Modeling
• Ads Targeting and Retrieval
• Advertiser Optimization
• Ads Marketplace Quality
• Ads Creative Effectiveness
• Ads Foundational Representations
• Ads Content Understanding
• Ads Ranking
• Feed Relevance
• Search and Answers Relevance
• ML Understanding
• Notifications Relevance

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

Pay Transparency:

This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave.

To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.

The base salary range for this position is: $185,800-$260,100 USD

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This is a highly collaborative role. You'll work closely across pre-training, post-training, and product with world-class researchers, infrastructure engineers, and designers.

As a researcher in this role, you'll be expected to:

• Own research projects on audio training, low-latency inference, and conversational responsiveness.
• Design and train large-scale models that natively support audio input and output.
• Investigate scaling behaviour such as how data, model size, and compute affect capability and efficiency.
• Build and maintain audio data pipelines, including preprocessing, filtering, segmentation, and alignment for training and evaluation.
• Collaborate with data and infrastructure teams to scale audio training efficiently across distributed systems.
• Publish and present research that moves the entire community forward.

Share code, datasets, and insights that accelerate progress across industry and academia.

This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports.

It's an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns.

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Key responsibilities include:

• Collaborating with a multidisciplinary team of software and hardware engineers to develop software defined radios;
• Implementing high-performance, real-time signal processing chains on embedded and hardware platforms to support mission-critical sensing capabilities;
• Developing Modeling and Simulation (M&S) code for RADAR techniques and data analysis including Hardware-in-the Loop / Software-in-the-loop (HIL/SIL) testing;
• Participating in laboratory and field testing of RF systems and techniques;
• Participating in the maturation of RF systems into deployable systems and products.

Required qualifications include:

• 5+ years of experience with a BSEE or related field;
• Strong foundation in digital signal processing, comms theory, and system engineering with emphasis in direction finding algorithm implementation;
• Hands-on experience with direction finding, angle-of-arrival estimation, and multi-antenna signal processing;
• Strong experience with DSP implementation for embedded devices including FPGA, Nvidia Jetson, and Software Defined Radios and/or software defined radios;
• Strong knowledge of Python and MATLAB;
• Experience with CUDA or GPU accelerated frameworks like cuSignal is preferred;
• Familiar with deep learning algorithms;
• Familiar with wireless communication standards (Bluetooth, 3G/4G/5G, Wi-Fi, SINCGARS, MUOS, etc.).

Preferred qualifications include:

• Masters or PhD degree in Electrical, Electronics, Computer Engineering, or related fields;
• Experience with ML frameworks such as TensorFlow and PyTorch;
• Defense, national security, or aerospace domain familiarity through industry or education;
• Extensive Digital Signal Processing (DSP) knowledge and experience;
• Expertise in Synthetic Aperture Radar (SAR) and/or Inverse SAR (ISAR): Image formation, waveforms, phenomenology, modeling and simulation.

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Our Public Sector Machine Learning team is focused on deploying cutting-edge models to mission-critical government systems through products like Donovan and Thunderforge. You will take state-of-the-art models developed internally and from the community, use them in production to solve problems for our customers and taskers.

Key responsibilities include:

• Improving and maintaining production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics
• Collaborating with product and research teams to identify and prototype ML-driven product enhancements, including for upcoming product lines
• Working with massive datasets to develop both generic models as well as fine-tune models for specific products
• Building scalable machine learning infrastructure to automate and optimize our ML services
• Serving as a cross-functional representative and advocate for machine learning techniques across engineering and product organizations

Ideal candidates will have extensive experience using computer vision, deep learning, and deep reinforcement learning, or natural language processing in a production environment. Solid background in algorithms, data structures, and object-oriented programming is also required.

Nice to haves include a graduate degree in Computer Science, Machine Learning, or Artificial Intelligence specialization, experience working with cloud platforms, and familiarity with ML evaluation frameworks and agentic model design.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training.

You'll also receive benefits including comprehensive health, dental, and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. This role may be eligible for additional benefits such as a commuter stipend.

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Your responsibilities will include:

• Designing, building, and deploying production-grade machine learning models and systems at scale
• Owning the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring
• Building scalable data and model pipelines with strong reliability, observability, and automated retraining
• Working with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems
• Partnering cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions

You'll work on a wide range of high-impact problems across the Reddit ecosystem, including recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, and multimodal embedding systems.

To be successful in this role, you'll need:

• 3-5+ years of experience building, deploying, and operating machine learning systems in production
• Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals
• ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)
• Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
• Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure
• Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions

Preferred qualifications include experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems, familiarity with distributed systems and large-scale data processing, and experience working with real-time systems and low-latency production environments.

At Reddit, we're committed to building a workforce representative of the diverse communities we serve. We're proud to be an equal opportunity employer and are committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures.

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As an HFC Fellow, you'll apply your academic and professional expertise to help design, evaluate, and interpret advanced generative AI systems,while gaining exposure to cutting-edge research and working alongside an interdisciplinary network of leading thinkers.

You'll get invited to engage in high-impact projects with our partnered AI labs and platforms, helping models understand real-world deep learning workflows by designing, reviewing, and optimising PyTorch models, evaluating complex ML code and AI-generated implementations for efficiency and correctness, and advising on GPU optimisation, scaling, and trade-offs.

Beyond the work, you'll become part of a supportive, interdisciplinary network of innovators and thought leaders committed to advancing frontier AI across domains.

You'll also contribute to research publications, collaborating with Scale's research team to co-author technical reports and research papers,boosting your academic visibility and professional recognition.

We're looking for individuals with a PhD or postdoctoral degree in Computer Science, Computer Engineering, or a related field, with 1-3+ years of experience as a Machine Learning Engineer or Data Scientist.

Key skills include strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow), experience with cloud infrastructure (AWS) and MLOps tools (Docker, Langchain), and a detail-oriented, innovative mindset with a passion in applied AI research and a commitment to collaboration.

Benefits include professional development, joining a top-tier network, flexible scheduling, and competitive pay.

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At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.

As an Applied Research Engineer at Labelbox, you will be at the forefront of developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers. Your role will involve designing and implementing advanced systems that align human feedback into AI training processes, such as Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), etc. You will also work on innovative techniques to measure and improve human data quality, and develop AI-assisted tools to enhance the data labeling process.

Your Impact

• Advance the field of AI alignment by developing cutting-edge methods, such as RLHF and novel approaches, that ensure AI systems reflect human preferences more accurately.

• Improve the quality of human-in-the-loop data by designing and deploying rigorous measurement and enhancement systems, leading to more reliable AI training.

• Increase efficiency and effectiveness in AI-assisted data labeling by creating tools that leverage active learning and adaptive sampling, reducing manual effort while improving accuracy.

• Shape the next generation of AI models by investigating how different types of human feedback (e.g., demonstrations, preferences, critiques) impact model performance and alignment.

• Optimize human feedback collection by developing novel algorithms that enhance how AI learns from human input, improving model adaptability and responsiveness.

• Bridge research and real-world application by integrating breakthroughs into Labelbox’s product suite, making human-AI alignment techniques scalable and impactful for users.

• Drive industry innovation by engaging with customers and the broader AI community to understand evolving data needs and share best practices for training frontier models.

• Contribute to the AI research ecosystem by publishing in top-tier journals, presenting at leading conferences, and influencing the future of human-centric AI.

• Stay ahead of AI advancements by continuously exploring new frontiers in human-AI collaboration, human data quality, and AI alignment, keeping Labelbox at the cutting edge.

• Establish Labelbox as a thought leader in AI by creating technical documentation, blog posts, and educational content that shape the industry's approach to human-centric AI development.

What You Bring

• A strong foundation in AI and machine learning, backed by a Ph.D. or Master’s degree in Computer Science, Machine Learning, AI, or a related field.

• Proven experience (3+ years) in solving complex ML challenges and delivering impactful solutions that improve real-world AI applications.

• Expertise in designing and implementing data quality measurement and refinement systems that directly enhance model performance and reliability.

• A deep understanding of frontier AI models,such as large language models and multimodal models,and the human data strategies needed to optimize them.

• Proficiency in Python and experience with deep learning frameworks like PyTorch, JAX, or TensorFlow to prototype and develop cutting-edge solutions.

• A track record of publishing in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL) and contributing to the broader research community.

• The ability to bridge research and application by interpreting new findings and rapidly translating them into functional prototypes.

• Strong analytical and problem-solving skills that enable you to tackle ambiguous AI challenges with structured, data-driven approaches.

• Exceptional communication and collaboration skills, allowing you to work effectively across multidisciplinary teams and with external stakeholders.

Labelbox Applied Research

At Labelbox Applied Research, we're committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advanced human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.

We foster an environment of intellectual curiosity, collaboration, and innovation. We encourage our researchers to explore new ideas, engage in open discussions, and contribute to the wider AI community through publications and conference presentations. Our goal is to be at the forefront of human-centric AI development, setting new standards for how AI systems learn from and interact with humans.

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The ideal candidate will have experience managing platform engineering teams with many cross-organizational customers, leading the development of large-scale distributed serving systems, and working with AI/ML inference technologies for online serving at Web scale.

Key responsibilities include:

• Leading the team to deliver continual improvements in advanced model architectures, cost-efficient resource utilization, and AI/ML developer productivity.
• Setting technical direction for the team based on company and org priorities.
• Coaching and developing talent on the team.

In return, you'll have the opportunity to work on a high-impact project that will shape the future of AI/ML at Pinterest.

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Responsibilities:

• Design, develop, and deploy Cresta's AI Agent solutions and proprietary models.
• Focus on practical AI challenges such as improving reasoning, planning capabilities, and evaluation in real-world scenarios.
• Collaborate with cross-functional teams including front-end and back-end software engineers to integrate AI Agents into Cresta's customer solutions.
• Lead initiatives to scale AI systems for production environments, ensuring performance and reliability across use cases.
• Contribute to solving cutting-edge problems in AI and help define the future roadmap for Cresta's AI Agents.
• Innovate and research ways to improve security, cost-efficiency, and reliability of AI systems.

Qualifications:

• Bachelor's or Master's Degree in Computer Science, Mathematics, or a related field.
• 2+ years of hands-on industry experience with AI and machine learning, preferably with experience working with LLMs in large-scale production environments.
• Solid knowledge of machine learning concepts and methods, especially those related to NLP, Generative AI, and working with LLMs.
• Practical knowledge of modern machine learning frameworks and technologies (e.g., PyTorch, Tensorflow, Hugging Face, NumPy), as well as experience with distributed systems and cloud-based AI infrastructure.
• Strong problem-solving and strategic thinking abilities, with a proven ability to lead cross-functional teams and work collaboratively to deliver innovative AI solutions in production.
• A passion for driving AI adoption and pushing the boundaries of AI technology into real-world applications, with an ability to mentor junior engineers and influence strategic decisions across the organization.

Perks & Benefits:

• We offer Cresta employees a variety of medical, dental, and vision plans, designed to fit you and your family's needs.
• Paid parental leave to support you and your family.
• Monthly Health & Wellness allowance.
• Work from home office stipend to help you succeed in a remote environment.
• Lunch reimbursement for in-office employees.
• PTO: 3 weeks in Canada.

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As an HFC Fellow, you'll apply your academic and professional expertise to help design, evaluate, and interpret advanced generative AI systems,while gaining exposure to cutting-edge research and working alongside an interdisciplinary network of leading thinkers.

Responsibilities:

• Engage in high-impact projects with our partnered AI labs and platforms.
• Design, review, and optimize PyTorch models.
• Evaluate complex ML code and AI-generated implementations for efficiency and correctness.
• Advise on GPU optimization, scaling, and trade-offs.
• Collaborate with Scale's research team to co-author technical reports and research papers.

Requirements:

• PhD or postdoctoral degree in Computer Science, Computer Engineering, or a related field.
• 1-3+ years of experience as a Machine Learning Engineer or Data Scientist.
• Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow).
• Experience with cloud infrastructure (AWS) and MLOps tools (Docker, Langchain) is a plus.

Benefits:

• Flexible schedule with 10–40 hour weeks.
• Competitive pay rates varying across platforms and depending on project scope, skillset, and location.
• Opportunity to work with a global network of engineers and experts to advance responsible AI.

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Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence.

The Role

In this role, you'll work with a team of elite researchers and engineers to design and implement post-training strategies that enhance Gemini’s capabilities in code security analysis. You will bring contributions to our ML innovation, post-training refinement (SFT/RLHF), advanced evaluation, and data generation to ensure our models can reliably perform safe and powerful code security analysis.

Key responsibilities:

• Design and Implement advanced post-training algorithms (SFT, RLHF, RLAIF) to optimize Gemini for code security tasks and secure coding practices.

• Diagnose and interpret training outcomes (regressions in coding ability, gains in security reasoning), and propose solutions to improve model capabilities.

• Actively monitor and evolve the system's performance through metric design.

• Develop reliable automated evaluation pipelines for code security that are strongly correlated with human security expert judgment.

• Construct complex benchmarks to probe the limits of the model’s ability to reason about control flow, memory safety, and software weakness.

About You

We are seeking individuals who excel in fast-pacing environments and are eager to contribute to the advancement of AI. We highly value the ability to invent novel solutions to complex problems, embracing a can-do and fail-fast mindset. We are looking for someone who genuinely believes in the future of AI and is committed to devoting their energy in this field.

In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:

• BSc, MSc or PhD/DPhil degree in computer science, stats, machine learning or similar experience working in industry

• Deep understanding of statistics is strongly preferred

• Experiences in fine-tuning and adaptation of LLMs (e.g. advanced prompting, supervised fine-tuning, RLHF)

• Strong knowledge of systems design and data structures

• Proven experience with TensorFlow, JAX, PyTorch, or similar leading deep learning frameworks

• Recent experience conducting applied research to improve the quality and training/serving efficiency of large transformer-based models

• A passion for Artificial Intelligence.

• Excellent communication skills and proven interpersonal skills, with a track record of effective collaboration with cross-functional teams

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The role is ideal for a senior modeler who also enjoys developing, productionizing models and leading technical direction across teams. The successful candidate will be able to provide technical leadership through design reviews, mentoring, and raising the quality bar for modeling and ML engineering practices.

In addition to the above responsibilities, the successful candidate will be expected to:

• Collaborate with infra/platform teams to ensure scalable, reliable training/serving (latency, cost, observability, rollout safety).
• Partner with signal-consuming teams (ranking, retrieval, integrity, ads) to define signal contracts, adoption patterns, and success metrics.
• Own the full ML lifecycle: data/labeling strategy (human labels + weak supervision), training pipelines, offline evaluation, online experimentation, deployment, and monitoring/retraining.
• Provide technical leadership through design reviews, mentoring, and raising the quality bar for modeling and ML engineering practices.

Nice to have: experience with Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring; familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.

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We're looking for a Machine Learning Intern to join our team in Toronto. As a Machine Learning Intern, you will work on tackling new challenges in machine learning and artificial intelligence. You will join our engineering teams as we maneuver through exponential growth and massive scale while building awesome products and features, creating visually rich experiences, spearheading the discovery problem, and pinpointing tomorrow's engineering challenges.

Responsibilities

• Lead your own project start to finish to contribute in cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems
• Collect, analyze, and synthesize findings from data and build intelligent data-driven models
• Write clean, efficient, and sustainable code
• Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across discovery, ads and search
• Scope and independently solve moderately complex problems
• Demonstrate accountability for the quality and completion of your tasks and projects, collaborating with your team and seeking guidance as needed

Requirements

• Working towards a Master's or PhD degree in Computer Science, ML, NLP, Statistics, Information Sciences or related field
• Machine Learning (ranking, computer vision, NLP, content recommendations, embedding, information retrieval etc)
• Experience with big data technologies (e.g., Hadoop/Spark) and scalable realtime systems that process stream data
• Strong interest in research and applying machine learning and AI to drive meaningful product innovation and user impact
• Exposure to ML, AI, data analytics, statistics, or related technical fields, through research, coursework, projects, or internships
• Proficiency in at least one systems language (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow)
• Experience in research and in solving analytical problems
• Strong communicator and team player with the ability to find solutions for open-ended problems

Why Intern at Pinterest?

• Meaningful Work: Contribute to projects that impact millions of users worldwide.
• Mentorship: Learn from and be guided by experienced engineers and researchers in the field.
• Growth and Development: Participate in professional development workshops and networking events to build your skills and connections.

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This role focuses on rapidly developing new features, and working across partner teams to deliver solutions, and maximize impact for Google and top customers.

You will be instrumental in translating cutting-edge AI research into real-world products, and demonstrating the capabilities of latest-generation models.

We are looking for engineers with a strong track record of building and shipping AI-powered software, ideally with experience in early-stage environments where they have contributed to scaling products from initial concept to production.

The ideal candidate will be motivated by the opportunity to drive product & business impact.

Key responsibilities:

• Harness frontier models to drive real-world high-impact outcomes

• Build evaluations, training data, and infrastructure to support AI deployments and rapid iterations

• Collaborate with researchers and product managers to translate research advancements into tangible product features.

• Contribute to the development of best practices for building and deploying generative AI applications.

• Contribute signal to influence the development of frontier models

• Lead the architecture and development of new products & features from 0 to 1.

About you:

In order to set you up for success as a Staff Research Engineer, Applied AI at Google DeepMind, we look for the following skills and experience:

Required Skills:

• Bachelor's degree or equivalent practical experience.

• 8 years of experience in software development, and with data structures/algorithms.

• 5 years of hands-on experience in AI research (e.g. RL, finetuning, evals), AI applications, or model deployment

• Proven experience in rapidly developing and shipping software products.

• Deep understanding of software development best practices, including testing & deployment.

• Experience with cloud computing platforms and infrastructure (e.g., Google Cloud Platform, AWS, Azure).

• Substantial experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Hugging Face, etc.

• Ability to work in a fast-paced environment and adapt to changing priorities.

Preferred Skills:

• Experience with generative AI research or applications.

• Contributions to open-source projects.

• Experience working in, or founding early stage startups.

• Experience delivering software solutions in a fast-paced, customer-facing environment.

If you are a passionate machine learning engineer with a drive to build innovative products and a desire to work at the forefront of AI, we encourage you to apply!

The US base salary range for this full-time position is between $197,000 - $291,000 + bonus + equity + benefits.

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As a Machine Learning Engineer on the Support Experience team, you'll play a crucial role in enhancing our self-serve support experiences.

About the Team

The Support Experience engineering organization builds and improves Stripe's user support from end to end: how users get help within our products, how they get in touch with us when they have questions, and how our teams use internal tools to answer those questions.

Responsibilities

• Design and implement state-of-the-art ML models and large-scale ML systems for enhancing self-serve support capabilities, balancing ML principles, domain knowledge, and engineering constraints
• Develop and optimize contextual conversation models and ML-powered resolution flows for common support scenarios, using tools such as PyTorch, TensorFlow, and XGBoost
• Create and refine pipelines for training and evaluating models in both offline and online environments, with a focus on improving support quality and user satisfaction
• Implement ML features that streamline information collection and processing for support agents, enhancing overall support efficiency
• Collaborate with product, strategy, and content teams to propose, prioritize, and implement new AI-driven support features and improve answer capabilities

Requirements

• Bachelor's Degree in ML/AI or related field (e.g. math, physics, statistics)
• 3+ years in AI/ML and backend engineering, including building and operating production ML systems at global scale with stringent SLOs,balancing reliability, latency, and cost,with privacy, security, and compliance by design.
• Deep and up-to-date applied LLM experience: RAG/embeddings, tool use/function calling, agentic planning/orchestration architectures, post-training methods, code generation, benchmarks and evaluations, etc.
• Familiarity with classical ML methods and common frameworks e.g. Pytorch, TensorFlow.
• Proficient in Python; strong distributed systems and data science fundamentals.
• Experience working closely with product management, design, other engineers, and other cross-functional partners.
• Strong technical leadership and communication: mentoring and elevating engineers, elevating AI/ML awareness and posture within organizations, setting architectural direction, and driving alignment in ambiguity.

Preferred Qualifications

• MS/PhD degree in ML/AI or related field (e.g. math, physics, statistics)
• Experience working in Java or Ruby codebases
• Experience designing, deploying, and owning Agentic LLM solutions (e.g., multi-step orchestrators, tool use/function calling) specifically for complex customer support or internal workflow automation.
• Comfortable working with distributed teams across multiple locations and time zones

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As a Machine Learning / AI Engineer, you will design, build, and productionize ML models for Search, Discovery, Ranking, Retrieval-Augmented Generation (RAG), and generative AI features. You will also build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows.

You will collaborate closely with engineers, researchers, designers, and product managers across multiple teams to deliver high-quality ML-driven features and infrastructure. This is a high-impact, cross-functional role where you will shape both foundational systems and user-facing capabilities.

Key responsibilities include:

• Design, build, and productionize ML models for Search, Discovery, Ranking, Retrieval-Augmented Generation (RAG), and generative AI features.
• Build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows.
• Collaborate with AI researchers to iterate on datasets, evaluation metrics, and model architectures to improve quality and relevance.
• Work with product engineers to define and deliver impactful AI features across Figma's platform.
• Partner with infrastructure engineers to develop and optimize systems for training, inference, monitoring, and deployment.
• Explore new ideas at the edge of what's technically possible and help shape the long-term AI vision at Figma.

Requirements include:

• 5+ years of industry experience in software engineering, with 3+ years focused on applied machine learning or AI.
• Strong experience with end-to-end ML model development, including training, evaluation, deployment, and monitoring.
• Proficiency in Python and familiarity with ML libraries like PyTorch, TensorFlow, Scikit-learn, Spark MLlib, or XGBoost.
• Experience designing and building scalable data and annotation pipelines, as well as evaluation systems for AI model quality.
• Experience mentoring or leading others and contributing to a culture of technical excellence and innovation.

Preferred qualifications include:

• Familiarity with search relevance, ranking, NLP, or RAG systems.
• Experience with AI infrastructure and MLOps, including observability, CI/CD, and automation for ML workflows.
• Experience working on creative or design-focused ML applications.
• Knowledge of additional languages such as C++ or Go is a plus, but not required.
• A product mindset with the ability to tie technical work to user outcomes and business impact.
• Strong collaboration and communication skills, especially when working across functions (engineering, product, research).

At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you're excited about this role but your past experience doesn't align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

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The ideal candidate will have expertise in multimodality and experience running large-scale experiments. They will be comfortable contributing to complex engineering systems and have a strong grasp of probability, statistics, and machine learning fundamentals.

This is an evergreen role, meaning that the position is open on an ongoing basis. The company receives many applications, and there may not always be an immediate role that aligns perfectly with the candidate's experience and skills. However, they encourage candidates to apply and continuously review applications.

Responsibilities:

• Own research projects on training and performance analysis of multimodal AI models.
• Curate and build large-scale datasets and evaluation benchmarks to advance vision capabilities.
• Work with data infrastructure engineers, pretraining researchers and engineers, and product teams to create frontier multimodal models and the products that leverage them.
• Publish and present research that moves the entire community forward.

Skills and Qualifications:

• Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor.
• Understanding of machine learning fundamentals, large-scale training, and distributed compute environments.
• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX).
• Comfortable with debugging distributed training and writing code that scales.
• Bachelor's degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.

Preferred qualifications include research or engineering contributions in visual reasoning, spatial understanding, or multimodal architecture design, experience developing evaluation frameworks for multimodal tasks, publications or open-source contributions in vision-language modeling, video understanding, or multimodal AI, and a strong grasp of probability, statistics, and ML fundamentals.

Logistics:

• Location: San Francisco, California.
• Compensation: $350,000 - $475,000 USD per year, depending on background, skills, and experience.
• Visa sponsorship: Yes.
• Benefits: Generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

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At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.

As an Applied Research Engineer at Labelbox, you'll sit at the junction of advanced AI research and real product impact, with a focus on the data that makes modern agents work,browser interactions, SWE/code traces, GUI sessions, and multi-turn workflows. You'll drive the data landscape required to advance capable, adaptable agents and help shape Labelbox's strategy for collecting, synthesizing, and evaluating it.

Create frameworks and tools to construct, train, benchmark and evaluate autonomous agent capabilities.

Design agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies.

Develop data pipelines from diverse sources like code repositories, web browsers, and computer systems.

Implement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models.

Engage with research teams in frontier AI labs and the wider AI community to understand evolving agent data needs for frontier models and share best practices.

Collaborate closely with frontier AI lab customers to understand requirements and guide model development.

Publish research findings in academic journals, conferences, and blog posts.

What You Bring

Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or related field.

At least 3 years of experience addressing sophisticated ML problems with successful delivery to customers.

Experience building and training autonomous agents,tool use, structured outputs, multi-step planning,across browsers/GUI, codebases, and databases using SFT and RL.

Constructed and evaluated agentic benchmarks (e.g. SWE-bench, WebArena, τ-bench, OSWorld) and reliability/efficiency suites (e.g. WABER).

Adept at interpreting research literature and quickly turning new ideas into prototypes.

Deep understanding of frontier models (autoregressive, diffusion), post-training (SFT, RLVR, RLAIF, RLHF, et al.), and their human data requirements.

Proficient in Python, data science libraries and deep learning frameworks (e.g., PyTorch, JAX, TensorFlow).

Strong analytical and problem-solving abilities in ambiguous situations.

Excellent communication skills.

Track record of publications in top-tier AI/ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, etc.).

Labelbox Applied Research

At Labelbox Applied Research, we're committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advanced human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.

Life at Labelbox

Location: Join our dedicated tech hubs in San Francisco or Wrocław, Poland

Work Style: Hybrid model with 2 days per week in office, combining collaboration and flexibility

Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making

Growth: Career advancement opportunities directly tied to your impact

Vision: Be part of building the foundation for humanity's most transformative technology

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Your responsibilities will include designing and implementing platform capabilities to support the AI/ML development and productionization lifecycle, including training, evaluation, deployment, monitoring, and management of models and agents.

You will also design and implement platform integrations with various frameworks in the AI/ML ecosystem, collaborate with the AI/ML community across the world to advance the state-of-the-art in AIOps, and ensure the latest AI/ML tooling advancements are available to Databricks' customers.

Additionally, you will mentor and guide junior engineers on the team by helping with project planning, technical decisions, and code and document review.

We are looking for a highly skilled and experienced software engineer with a strong background in AI/ML and a passion for building and maintaining open-source platforms.

The ideal candidate will have a BS (or higher) in Computer Science, or a related field, and 5+ years of hands-on experience in building production systems using at least one of the following programming languages: Python (Preferred), Scala and Java.

Experience building and maintaining software tools and frameworks for AI/ML, ideally in an open-source environment, is also required.

Familiarity with AI/ML and AIOps concepts and technologies, such as model training, deployment, and monitoring, is essential.

A deep understanding and experience in working with agent frameworks such as LangChain, LlamaIndex, DSPy, or other similar projects is preferred.

Significant contributions to open-source projects in the AI/ML domain, such as SparkML, TensorFlow, PyTorch, MLflow, or other similar projects, are also preferred.

At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees.

For specific details on the benefits offered in your region, please click here.

We are committed to diversity and inclusion and welcome applications from candidates of all backgrounds.

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The impact you'll have:

• Design and implement platform capabilities to support the AI/ML development and productionization lifecycle including training, evaluation, deployment, monitoring, and management of models and agents
• Design and implement platform integrations with various frameworks in the AI/ML ecosystem
• Collaborate with the AI/ML community across the world to advance the state-of-the-art in AIOps
• Ensure the latest AI/ML tooling advancements are available to Databricks' customers, thereby enabling organizations around the world to get more value from their data
• Mentor and guide junior engineers on the team by helping with project planning, technical decisions, and code and document review

What we look for:

• BS (or higher) in Computer Science, or a related field
• 3+ years of hands-on experience in building production systems using at least one of the following programming languages: Python (Preferred), Scala and Java
• Experience building and maintaining software tools and frameworks for AI/ML, ideally in an open-source environment
• Familiarity with AI/ML and AIOps concepts and technologies, such as model training, deployment, and monitoring
• Deep understanding and experience in working with agent frameworks such as LangChain, LlamaIndex, DSPy, or other similar projects
• Significant contributions to open-source projects in the AI/ML domain, such as SparkML, TensorFlow, PyTorch, MLflow, or other similar projects

About Databricks

Databricks is the data and AI company. More than 10,000 organizations worldwide , including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 , rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI.

Benefits

At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.

Our Commitment to Diversity and Inclusion

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In this role, you will:

Perform manual and automated testing of web applications, APIs, and mobile apps (iOS/Android). Conduct network and cloud level assessments with various tooling. Triage and validate reports from automated scanners or bug bounty hunters to eliminate false positives and escalate true positives. Perform initial prompt injection and jailbreak tests on AI prototypes, services, and applications using established checklists (OWASP Top 10 for LLMs). Draft high-quality reports that detail the "path to compromise" with clear, reproducible steps for developers. Manage and update the team's testing infrastructure (e.g., Burp Suite, and basic C2 listeners). Provide direct technical guidance to engineering teams on how to patch vulnerabilities like XSS, SQLi, and IDOR. Design and lead multi-week Red Team operations that mimic specific threat actors (APTs) to test the SIRT detection capabilities. Build custom payloads, droppers, and obfuscated scripts to bypass EDR/AV and maintain stealth. Build automated testing frameworks for AI systems (e.g., using PyRIT, Promptfoo, or Garak) to test for models related to sensitive data leakage. Execute sophisticated attacks against AWS/Azure/K8s, focusing on IAM misconfigurations and container escapes. Collaborate with SIRT and Detection Engineering to tune SIEM alerts based on the techniques used during an engagement. Oversee the organization's bug bounty program, identifying trends in submissions to suggest broad architectural security changes.

Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply.

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The Listings and Host Tools Data and AI team owns data pipelines and ML models and builds services for serving that are used in the above areas. We leverage open source, third-party, and homegrown ML models to improve the Host and Guest experience.

As an ML engineer, you will partner closely with our data science, product partners, and other ML + data engineers on the team to execute on these opportunities in order to improve the Host and Guest product experience on Airbnb.

Your responsibilities will include:

• Working with large-scale structured and unstructured data to build and continuously improve cutting-edge Machine Learning models for Airbnb product, business, and operational use cases.

• Collaborating with cross-functional partners, including software engineers, product managers, operations, and data scientists, to identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.

• Prototyping machine learning use cases for use in the product and working with stakeholders to iterate on requirements.

• Developing, productionizing, and operating Machine Learning models and pipelines at scale, including both batch and real-time use cases.

• Designing and building services and APIs to enable serving ML model-driven data to product use cases.

We're looking for someone with 8+ years of industry experience in applied Machine Learning, including a Master's or Ph.D. in a relevant field. You should have experience in both Natural Language Processing and Computer Vision, as well as strong programming and data engineering skills.

You should also have a deep understanding of Machine Learning best practices, algorithms, and domains, as well as experience with technologies such as TensorFlow, PyTorch, Kubernetes, Spark, Airflow, and data warehouses.

If you're passionate about building end-to-end Machine Learning infrastructure and productionizing Machine Learning models, we'd love to hear from you!

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Responsibilities:

• Design, develop, and apply DS solutions to inform improvements in advertiser experience and Reddit's ad platform
• Analyze large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform
• Collaborate with product managers and engineers to define product requirements and translate them into data science solutions
• Develop ML models & DS methods to improve anomaly detection, prediction, & pattern recognition
• Communicate findings and recommendations to stakeholders across the organization
• Stay up-to-date on the latest advancements in machine learning and data science
• Mentor and guide junior data scientists on the team

Qualifications:

• Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research
• For M.S. holders: 5+ years of industry experience in applied science or data science roles
• For Ph.D. holders: 4+ years of industry experience in applied science or data science roles
• Platform experience and a deep understanding of the ads ecosystem
• Strong understanding of statistical modeling, machine learning algorithms, causal inference and experimental design
• Experience with large-scale data processing and analysis using tools such as Spark, Hadoop, or Hive; knowledge of BigQuery a plus
• Proficiency in Python or R and experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch
• Experience with SQL and relational databases
• Excellent communication and presentation skills

Bonus Points:

• Experience with online advertising and ad tech
• Experience with causal inference and A/B testing
• Contributions to open-source projects or publications in relevant conferences or journals

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

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We are looking for an experienced engineer with 8+ years of experience in ML infrastructure, including model training and model deployments. You should have hands-on experience with ML optimization, cloud-based technologies, MLOps tools, and proficiency with common programming languages and frameworks of ML. Strong focus on scalability, reliability, performance, and ease of use is essential.

In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave.

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We achieve this by delivering highly personalised and relevant content and product experiences to the Airbnb community, both on and off of the Airbnb platform. The north star is full autonomy , where AI identifies opportunities, creates campaigns, personalises experiences, and optimises outcomes with minimal human intervention.

As a machine learning engineer or scientist, your expertise will be pivotal in developing AI-powered solutions to shape the future of the Airbnb agentic growth platform with cutting-edge AI techniques. You will drive and guide the rest of the engineers to brainstorm, design and develop AI products and features from inception to production.

Some example projects you will work on:

• AI-Powered Content Generation
• ML/AI Orchestration for Decisioning
• Proactive Marketing Analyst Agent

A typical day will involve working with large scale structured and unstructured data; exploring, experimenting, building and continuously improving Machine Learning models and pipelines for Airbnb product, business and operational use cases.

You will work collaboratively with cross-functional partners including product managers, operations and data scientists, to identify opportunities for business impact; understand, refine, and prioritise requirements for machine learning, and drive engineering decisions.

Hands-on develop, productionise, and operate ML/AI models and pipelines at scale, including both batch and real-time use cases.

Leverage third-party and in-house Machine Learning tools & infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.

Collaborate actively with engineers to apply ML / AI in their solutions to help validate ideas and guide to the right outcomes.

Partner with ML/AI Engineers in foundations engineering to mentor and develop initiatives that make ML/AI applications a core discipline for non-ML/AI engineers.

Your expertise will be crucial in developing AI-powered solutions to shape the future of the Airbnb agentic growth platform with cutting-edge AI techniques.

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You will work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.

Key responsibilities include:

• Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters.
• Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.
• Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows.
• Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.

You may be a good fit if you:

• Are proficient in Python and async/concurrent programming with frameworks like Trio.
• Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX).
• Have industry experience in machine learning research.
• Can balance research exploration with engineering implementation.
• Enjoy pair programming (we love to pair!).
• Care about code quality, testing, and performance.
• Have strong systems design and communication skills.
• Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems.

Strong candidates may have:

• Familiarity with LLM architectures and training methodologies.
• Experience with reinforcement learning techniques and environments.
• Experience with virtualization and sandboxed code execution environments.
• Experience with Kubernetes.
• Experience with distributed systems or high-performance computing.
• Experience with Rust and/or C++.

Strong candidates need not have:

• Formal certifications or education credentials.
• Academic research experience or publication history.

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Our team is responsible for building systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes. You will work on high-impact systems that improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems.

As a Senior Machine Learning Engineer, you will:

• Design, build, and deploy production-grade machine learning models and systems at scale
• Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring
• Build scalable data and model pipelines with strong reliability, observability, and automated retraining
• Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems
• Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions
• Improve system performance across latency, throughput, and model quality metrics
• Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph & transformers based, and LLM evaluation/alignment

Basic Qualifications:

• 3-5+ years of experience building, deploying, and operating machine learning systems in production
• Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals
• ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)
• Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
• Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure
• Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions
• Experience improving measurable metrics through applied machine learning

Preferred Qualifications:

• Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems
• Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)
• Experience working with real-time systems and low-latency production environments
• Background in feature engineering, model optimization, and production monitoring
• Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale
• Advanced degree in Computer Science, Machine Learning, or related quantitative field

Potential Teams:

• Ads Measurement Modeling
• Ads Targeting and Retrieval
• Advertiser Optimization
• Ads Marketplace Quality
• Ads Creative Effectiveness
• Ads Foundational Representations
• Ads Content Understanding
• Ads Ranking
• Feed Relevance
• Search and Answers Relevance
• ML Understanding
• Notifications Relevance

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

Pay Transparency:

This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/. To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is $216,700-$303,400 USD

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Responsibilities:

• Analyze large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform
• Develop ML models & DS methods to for improved anomaly detection, prediction, pattern recognition
• Communicate findings and recommendations to stakeholders across the organization
• Collaborate with product, engineering, sales, and marketing partners to define product and program requirements and translate them into data science solutions
• Stay up-to-date on the latest advancements in machine learning and data science

Qualifications:

• Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research
• For M.S. holders: 3+ years of industry experience in applied science or data science roles
• For Ph.D. holders: 2+ years of industry experience in applied science or data science roles
• Strong understanding of statistical modeling, machine learning algorithms, causal inference and experimental design
• Experience with large-scale data processing and analysis using tools such as Spark, Hadoop, or Hive; knowledge of BigQuery a plus
• Proficiency in Python or R and experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch
• Experience with SQL and relational databases
• Excellent communication and presentation skills

Bonus Points:

• Experience with online advertising and ad tech
• Experience with causal inference and A/B testing
• Contributions to open-source projects or publications in relevant conferences or journals

Benefits:

• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Comprehensive Medical Benefits & Health Care Spending Account
• Registered Retirement Savings Plan with matching contributions
• Income Replacement Programs
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

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In this role, you will specialize in improving and optimizing our ads auction and pricing mechanism which will have a direct impact on upleveling the utility for both our advertiser and user values. You will also have the opportunity to work on other org-wide strategic initiatives such as supply optimization and ad relevance, where you will drive and execute on Reddit’s vision to transform Reddit into an advertising platform that shows the right ads to the right users at the right time in the right context.

As a Staff Machine Learning Engineer in the Ads Marketplace Quality team, you will be an industry technical leader with domain knowledge in ads marketplace dynamics, auction and pricing, you will research, formulate, and execute on our mission to build end-to-end algorithmic solutions and deliver values to all the three-sided participants to our marketplace.

Responsibilities:

• Lead and oversee the strategy development, quarterly planning and day-to-day execution of initiatives related to ads marketplace, auction and pricing.
• Proactively further our understanding of marketplace dynamics and develop algorithms to improve the efficiency and effectiveness of our ads marketplace, auction and pricing.
• Oversee end-to-end ML workflows,from data ingestion and feature engineering to model training, evaluation, and deployment,that optimizes the ads marketplace efficiency.
• Be a mentor, lead both junior and senior engineers in implementing technical designs and reviews. Fostering a culture of innovation, technical excellence, and knowledge sharing across the organization.
• Be a cross-functional advocate for the team, collaborate with cross-functional teams (e.g., product management, data science, PMM, Sales etc.) to innovate and build products.

Required Qualifications:

• 8+ years of experience with industry-level product development, with at least 5+ years focused on data-driven, marketplace-optimization problem space at scale.
• Strong knowledge of ads marketplace optimization. Demonstrated experience architecting ads marketplace design, improving and optimizing ads auction and pricing mechanisms.
• Solid understanding of large-scale data processing, distributed computing, and data infrastructure (e.g., Spark, Kafka, Beam, Flink).
• Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries for feature engineering, model training, and inference.
• Proficiency with programming languages (Java, Python, Golang, C++, or similar) and statistical analysis.
• Proven technical leadership in cross-functional settings, driving architectural decisions and influencing stakeholders (product, data science, privacy, legal).
• Excellent communication, mentoring, and collaboration skills to align teams on a long-term vision for ads marketplace optimization.

Benefits:

• Comprehensive Healthcare Benefits
• 401k Matching
• Workspace benefits for your home office
• Personal & Professional development funds
• Family Planning Support
• Flexible Vacation (please use them!) & Reddit Global Wellness Days
• 4+ months paid Parental Leave
• Paid Volunteer time off

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As a senior technical individual contributor, you will partner closely with our leaders across the broader technical organization to design, execute and deliver in a complex and collaborative roadmap of Trust engineering efforts.

Your expertise will be crucial in defining and executing on the long-term ML technical vision and strategy for the Trust organization, identifying key investments, architecting scalable solutions, and championing best practices that advance the state-of-the-art in production ML systems.

You will serve as a technical leader and mentor to other ML and software engineers across the organization, providing guidance on complex architectural and modeling challenges, and raising the overall technical bar.

Key responsibilities include:

• Defining and executing on the long-term ML technical vision and strategy for the Trust organization
• Serving as a technical leader and mentor to other ML and software engineers across the organization
• Driving and delivering large-scale, multi-quarter ML initiatives that span multiple teams
• Working with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases
• Collaborating with cross-functional partners to identify opportunities for business impact and understand, refine, and prioritize requirements for machine learning models

Requirements include:

• 12+ years of industry experience in applied Machine Learning
• 2-3+ years working with LLMs and novel GenAI technologies
• Proficiency and proven experience on Agentic AI (frameworks, orchestration, architecture and productionization)
• A Bachelor’s, Master’s or PhD in CS/ML or related field
• Strong programming (Scala / Python / Java/ C++ or equivalent) and data engineering skills
• Deep understanding of Machine Learning best practices, algorithms, and domains
• Experience with AgenticAI, Tensorflow, PyTorch, Kubernetes, and industry experience building end-to-end Machine Learning and Agentic infrastructure

Our Commitment To Inclusion & Belonging: Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions.

How We'll Take Care of You: Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.

Pay Range $244,000-$305,000 USD

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As part of the Safeguards team, you'll design and implement ML infrastructure that powers Claude safety. Your work will directly contribute to making AI systems more trustworthy and aligned with human values, ensuring our models operate safely as they become more capable.

Responsibilities:

• Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem
• Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications
• Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems
• Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards
• Implement automated testing, deployment, and rollback systems for ML models in production safety applications
• Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs
• Contribute to the development of internal tools and frameworks that accelerate safety research and deployment

You may be a good fit if you:

• Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment
• Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX
• Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)
• Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads
• Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems)
• Are results-oriented, with a bias towards reliability and impact in safety-critical systems
• Enjoy collaborating with researchers and translating cutting-edge research into production systems
• Care deeply about AI safety and the societal impacts of your work

Strong candidates may have experience with:

• Working with large language models and modern transformer architectures
• Implementing A/B testing frameworks and experimentation infrastructure for ML systems
• Developing monitoring and alerting systems for ML model performance and data drift
• Building automated labeling systems and human-in-the-loop workflows
• Experience in trust & safety, fraud prevention, or content moderation domains
• Knowledge of privacy-preserving ML techniques and compliance requirements
• Contributing to open-source ML infrastructure projects

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.

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Location: Remote-USA

The Payments team at Airbnb is responsible for everything related to settling money in Airbnb's global marketplace. As a Senior Machine Learning Engineer for Payments, you will be the catalyst that transforms bold AI innovation into production systems that make Airbnb Payment experience feel effortless and secure.

Responsibilities:

• Spearhead LLM agents, real-time anomaly detectors, and other breakthrough solutions that solve real-world problems and create product magic.

• Collaborate with product, engineering, ops, and data science to spot high-leverage opportunities, refine AI/ML requirements, make principled architecture choices, and measure business value with clear, data-driven metrics.

• Design, train, deploy, and operate large-scale AI applications for both batch and streaming workloads, ensuring low latency, high reliability, and continuous improvement via automated monitoring and retraining loops.

• Mentor and inspire teammates, fostering a collaborative, experimentation-driven environment where cutting-edge research meets production excellence and every engineer is empowered to push AI boundaries at Airbnb.

Your Expertise:

• 5+ years of industry experience in applied AI/ML, inclusive MS or PhD in relevant fields.

• Strong programming (Python/Java) and data engineering skills.

• Proven mastery of modern AI/LLM workflows , prompt engineering, fine-tuning (LoRA, RLHF), hallucination mitigation, safety guardrails, and rigorous online/offline testing to minimize training/inference drift and ensure reliable outcomes.

• Hands-on experience with at least three of the following: PyTorch/TensorFlow, scalable inference stacks, vector search, orchestration/MLOps platforms (Kubeflow, Airflow), large-scale data streaming & processing (Spark, Ray, Kafka).

• Demonstrated success designing, deploying, and monitoring production AI systems , e.g., personalization engines, generative content services , complete with drift/cost/latency monitoring, automated retraining triggers, and cross-functional collaboration that translates ambiguous business needs into measurable AI impact.

• Prior knowledge of AI/ML applications in the Payments domain is highly desirable.

Our Commitment To Inclusion & Belonging:

Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively led people, and to develop the best products, services, and solutions.

How We'll Take Care of You:

Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs, and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.

Pay Range: $191,000-$223,000 USD

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You will work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.

Some representative projects include:

• Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters.
• Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.
• Drive performance improvements across our stack through profiling, optimization, and benchmarking.
• Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.

You may be a good fit if you:

• Are proficient in Python and async/concurrent programming with frameworks like Trio.
• Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX).
• Have industry experience in machine learning research.
• Can balance research exploration with engineering implementation.
• Enjoy pair programming (we love to pair!).
• Care about code quality, testing, and performance.
• Have strong systems design and communication skills.
• Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems.

Strong candidates may have:

• Familiarity with LLM architectures and training methodologies.
• Experience with reinforcement learning techniques and environments.
• Experience with virtualization and sandboxed code execution environments.
• Experience with Kubernetes.
• Experience with distributed systems or high-performance computing.
• Experience with Rust and/or C++.

Strong candidates need not have:

• Formal certifications or education credentials.
• Academic research experience or publication history.

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This position is needed to scope, design, and deploy machine learning systems into the real world. The individual will closely partner with Product & Engineering teams to execute the roadmap for Twilio's AI/ML products and services.

You will understand customers' needs, build data products that work at a global scale, and own end-to-end execution of large-scale ML solutions.

To thrive in this role, you must have a deep background in ML engineering and a consistent track record of solving data & machine-learning problems at scale.

Responsibilities:

• Build and maintain scalable machine learning solutions in production
• Train and validate both deep learning-based and statistical-based models considering use-case, complexity, performance, and robustness
• Demonstrate end-to-end understanding of applications and develop a deep understanding of the 'why' behind our models & systems
• Partner with product managers, tech leads, and stakeholders to analyze business problems, clarify requirements, and define the scope of the systems needed
• Work closely with data platform teams to build robust scalable batch and real-time data pipelines
• Collaborate with software engineers, build tools to enhance productivity, and to ship and maintain ML models
• Drive high engineering standards on the team through mentoring and knowledge sharing
• Uphold engineering best practices around code reviews, automated testing, and monitoring

Qualifications:

• 7+ years of applied ML experience with proficiency in Python
• Strong background in the foundations of Machine Learning and building blocks of modern Deep Learning
• Track record of building, shipping, and maintaining Machine Learning models in production in an ambiguous and fast-paced environment
• Track record of designing and architecting large-scale experiments and analysis to inform product roadmap
• Familiarity with ML Ops concepts related to testing and maintaining models in production such as testing, retraining, and monitoring
• Demonstrated ability to ramp up, understand, and operate effectively in new application/business domains
• Experience working in an agile team environment with changing priorities
• Experience of working on AWS

Desired:

• Experience with Large Language Models

Travel:

We prioritize connection and opportunities to build relationships with our customers and each other. For this role, you may be required to travel occasionally to participate in project or team in-person meetings.

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This role focuses on rapidly developing new features, and working across partner teams to deliver solutions, and maximize impact for Google and top customers.

You will be instrumental in translating cutting-edge AI research into real-world products, and demonstrating the capabilities of latest-generation models.

We are looking for engineers with a strong track record of building and shipping AI-powered software, ideally with experience in early-stage environments where they have contributed to scaling products from initial concept to production.

The ideal candidate will be motivated by the opportunity to drive product & business impact.

Key responsibilities:

• Harness frontier models to drive real-world high-impact outcomes

• Build evaluations, training data, and infrastructure to support AI deployments and rapid iterations

• Collaborate with researchers and product managers to translate research advancements into tangible product features.

• Contribute to the development of best practices for building and deploying generative AI applications.

• Contribute signal to influence the development of frontier models

• Lead the architecture and development of new products & features from 0 to 1.

About you:

In order to set you up for success as a Staff Research Engineer, Applied AI at Google DeepMind, we look for the following skills and experience:

Required Skills:

• Bachelor's degree or equivalent practical experience.

• 8 years of experience in software development, and with data structures/algorithms.

• 5 years of hands-on experience in AI research (e.g. RL, finetuning, evals), AI applications, or model deployment

• Proven experience in rapidly developing and shipping software products.

• Deep understanding of software development best practices, including testing & deployment.

• Experience with cloud computing platforms and infrastructure (e.g., Google Cloud Platform, AWS, Azure).

• Substantial experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Hugging Face, etc.

• Ability to work in a fast-paced environment and adapt to changing priorities.

Preferred Skills:

• Experience with generative AI research or applications.

• Contributions to open-source projects.

• Experience working in, or founding early stage startups.

• Experience delivering software solutions in a fast-paced, customer-facing environment.

If you are a passionate machine learning engineer with a drive to build innovative products and a desire to work at the forefront of AI, we encourage you to apply!

The US base salary range for this full-time position is between $197,000 - $291,000 + bonus + equity + benefits.

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Key Responsibilities:

• Shape the direction of our applied AI areas and intelligence features in our products.
• Drive the development and deployment of state-of-the-art AI models and systems that directly impact the capabilities and performance of Databricks' products and services.
• Develop novel data collection, fine-tuning, and LLM technologies that achieve optimal performance on specific tasks and domains.
• Design and implement ML pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid experimentation and iteration.
• Work closely with cross-functional teams, including AI researchers, ML engineers, and product teams, to deliver impactful AI solutions that enhance user productivity and satisfaction.
• Build scalable, reusable backend systems to support GenAI products across the company.

What We’re Looking For:

• 2-8 years of machine learning engineering experience in high-velocity, high-growth companies.
• Strong track record of working with language modeling technologies.
• Proficiency in Python, TensorFlow/PyTorch, and scalable ML architectures.
• Ability to drive end-to-end model development, from research and prototyping to deployment and monitoring.
• Strong analytical and problem-solving skills, with a passion for improving AI-driven user experiences.
• Strong coding and software engineering skills, and familiarity with software engineering principles around testing, code reviews and deployment.

Why Join Us?

At Databricks, we are building state-of-the-art AI solutions that redefine how users interact with data and our products. You’ll have the opportunity to shape the future of AI-driven products at Databricks, work with cutting-edge models, and collaborate with a world-class team of AI and ML experts.

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range for this role is $190,000-$285,000 USD.

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About Us

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence.

The Role

In this role, you'll work with a team of elite researchers and engineers to design and implement post-training strategies that enhance Gemini’s capabilities in code security analysis. You will bring contributions to our ML innovation, post-training refinement (SFT/RLHF), advanced evaluation, and data generation to ensure our models can reliably perform safe and powerful code security analysis.

Key responsibilities:

• Design and Implement advanced post-training algorithms (SFT, RLHF, RLAIF) to optimize Gemini for code security tasks and secure coding practices.

• Diagnose and interpret training outcomes (regressions in coding ability, gains in security reasoning), and propose solutions to improve model capabilities.

• Actively monitor and evolve the system's performance through metric design.

• Develop reliable automated evaluation pipelines for code security that are strongly correlated with human security expert judgment.

• Construct complex benchmarks to probe the limits of the model’s ability to reason about control flow, memory safety, and software weakness.

About You

We are seeking individuals who excel in fast-pacing environments and are eager to contribute to the advancement of AI. We highly value the ability to invent novel solutions to complex problems, embracing a can-do and fail-fast mindset. We are looking for someone who genuinely believes in the future of AI and is committed to devoting their energy in this field.

In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:

• BSc, MSc or PhD/DPhil degree in computer science, stats, machine learning or similar experience working in industry

• Deep understanding of statistics is strongly preferred

• Experiences in fine-tuning and adaptation of LLMs (e.g. advanced prompting, supervised fine-tuning, RLHF)

• Strong knowledge of systems design and data structures

• Proven experience with TensorFlow, JAX, PyTorch, or similar leading deep learning frameworks

• Recent experience conducting applied research to improve the quality and training/serving efficiency of large transformer-based models

• A passion for Artificial Intelligence.

• Excellent communication skills and proven interpersonal skills, with a track record of effective collaboration with cross-functional teams

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You will collaborate closely with technical teams to support the development and refinement of new AI capabilities, with a primary focus on quantitative financial risk management domains. Your expertise will drive the selection and rigorous resolution of complex risk-related problems, including market risk modeling, credit and counterparty risk, liquidity and funding risk, operational and model risk, stress testing & scenario analysis, Value at Risk (VaR)/Expected Shortfall (ES), risk attribution, capital allocation (economic/regulatory), and enterprise-wide risk frameworks under regulatory regimes (Basel, Dodd-Frank, IFRS 9, etc.).

This role requires exceptional quantitative rigor, rapid adaptation to evolving guidelines, and the ability to deliver precise, technically sound critiques, derivations, and solutions in a fast-paced environment. As a Finance Risk Expert, you will directly support xAI's mission by helping train and refine frontier AI models. You will teach the models how risk professionals quantify uncertainties, model tail events, assess portfolio vulnerabilities, ensure regulatory compliance, perform stress testing, and make data-driven decisions to protect capital and maintain financial stability.

Your tasks may include recording audio walkthroughs of risk models, participating in video-based scenario reasoning, or producing detailed quantitative risk analysis traces. All outputs are considered work-for-hire and owned by xAI.

Responsibilities:

• Use proprietary annotation and evaluation software to deliver accurate labels, rankings, critiques, and comprehensive solutions on assigned projects
• Consistently produce high-quality, curated data that adheres to strict quantitative and regulatory standards
• Collaborate with engineers and researchers to develop and iterate on new training tasks, risk-specific benchmarks, and evaluation frameworks
• Provide constructive feedback to improve the efficiency, precision, and usability of annotation and data-collection tools
• Select and solve challenging problems from financial risk domains where you have deep expertise

Basic Qualifications:

• Master’s or PhD in a quantitative discipline: Quantitative Finance, Financial Engineering, Financial Mathematics, Statistics, Applied Mathematics, Econometrics, Risk Management, Operations Research, Physics, Computer Science (with risk/finance focus), or closely related field or equivalent professional experience as a quantitative risk analyst, risk modeler, or risk quant
• Excellent written and verbal English communication (technical reports, regulatory documentation, explanatory breakdowns)
• Strong familiarity with financial risk data sources and platforms (Bloomberg, Refinitiv, Moody’s Analytics, S&P Capital IQ, RiskMetrics, internal bank risk systems, regulatory filings, Basel/FRB datasets, etc.)
• Exceptional analytical reasoning, attention to detail, and ability to exercise sound judgment with incomplete or ambiguous data

Preferred Skills and Experience:

• Professional experience in quantitative risk management, model development/validation, or risk analytics at a bank, hedge fund, asset manager, insurance company, regulator, or consulting firm
• Track record of publication(s) or contributions in refereed journals/conferences on risk, econometrics, statistics, or quantitative finance
• Prior teaching, mentoring, or training experience (university, industry workshops, regulatory training)
• Proficiency in Python/R for risk modeling (pandas, NumPy, SciPy, statsmodels, QuantLib, PyTorch/TensorFlow for ML risk models, etc.) and familiarity with risk systems (Murex, Calypso, Numerix, etc.)
• Experience with Monte Carlo simulation, copula models, stochastic processes, time-series analysis, extreme value theory, or machine learning for risk (anomaly detection, credit scoring, etc.)
• Knowledge of regulatory capital frameworks (Basel III/IV, FRB CCAR, SR 11-7 model risk guidance, IFRS 9/CECL, Solvency II)
• CFA, FRM, PRM, CQF, or similar risk-focused certifications
• Previous exposure to large language models, AI safety, or quantitative evaluation pipelines

Location and Other Expectations:

• Tutor roles may be offered as full-time, part-time, or contractor positions, depending on role needs and candidate fit
• For contractor positions, hours will vary widely based on project scope and contractor availability, with no fixed commitments required
• Tutor roles may be performed remotely from any location worldwide, subject to legal eligibility, time-zone compatibility, and role specific needs
• For US based candidates, please note we are unable to hire in the states of Wyoming and Illinois at this time
• We are unable to provide visa sponsorship
• For those who will be working from a personal device, your computer must meet xAI’s minimum hardware requirements

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You will collaborate closely with our technical teams to support the development and refinement of new AI capabilities, with a particular emphasis on quantitative trading domains.

Your expertise will help select and solve challenging problems in systematic and quantitative strategies , including statistical arbitrage, factor investing, market microstructure modeling, high-frequency / execution algorithms, risk premia harvesting, machine learning-based alpha generation, and portfolio optimisation under realistic constraints.

This role requires strong analytical thinking, rapid adaptation to evolving guidelines, and the ability to provide rigorous, technically sound critiques and solutions in a fast-moving environment.

As a Quantitative Trader, you will directly contribute to xAI's mission by helping train and refine our frontier AI models.

You will teach the models how quantitative traders reason, model markets, evaluate signals, manage risk, and interact with complex financial data and systems.

This involves providing high-quality data in various formats (text, voice, video), writing detailed annotations, critiquing model outputs, recording audio explanations, and occasionally participating in structured video sessions.

We are looking for individuals who are enthusiastic about these data-generation activities, as they form a core part of advancing xAI’s goals in scientific discovery and real-world reasoning.

Quantitative Traders provide labeling, annotation, evaluation, and expert reasoning services across text, voice, and video data modalities to support model training and evaluation.

The role may include recording audio responses, participating in video-based tasks, or producing step-by-step quantitative reasoning traces , all of which are essential job functions required to fulfill xAI’s mission.

All outputs are considered work-for-hire and owned by xAI.

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While experience in one or more of the following areas is a strong plus: large-scale model training, research in 3D computer vision.

You will collaborate closely with researchers, engineers, and product teams to bring advanced 3D modeling and machine learning techniques into real-world applications, ensuring that our technology remains at the forefront of visual innovation. This role involves significant hands-on research and engineering work, driving projects from conceptualization through to production deployment.

Key responsibilities include designing, implementing, and training large-scale diffusion models for generating 3D worlds, developing and experimenting with large-scale diffusion models to add novel control signals, adapting to target aesthetic preferences, or distilling for efficient inference, collaborating closely with research and product teams to understand and translate product requirements into effective technical roadmaps, contributing hands-on to all stages of model development including data curation, experimentation, evaluation, and deployment, continuously exploring and integrating cutting-edge research in diffusion and generative AI more broadly, acting as a key technical resource within the team, mentoring colleagues, and driving best practices in generative modeling and ML engineering.

Ideal candidate profile includes 3+ years of experience in generative modeling or applied ML roles, extensive experience with machine learning frameworks such as PyTorch or TensorFlow, especially in the context of diffusion models and other generative models, deep expertise in at least one area of generative modeling, strong history of publications or open-source contributions involving large-scale diffusion models, strong coding proficiency in Python and experience with GPU-accelerated computing, ability to engage effectively with researchers and cross-functional teams, clearly translating complex technical ideas into actionable tasks and outcomes, comfortable operating within a dynamic startup environment with high levels of ambiguity, ownership, and innovation.

Nice to have includes contributions to open-source projects in the fields of computer vision, graphics, or ML, familiarity with large-scale training infrastructure, experience integrating machine learning models into production environments, led or been involved with the development or training of large-scale, state-of-the-art generative models.

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Design & own evaluation/optimization frameworks for frontier music models. Dive deep under the hood of our music generation systems, applying computational & human resources to understand model capabilities and identify areas for growth. Build optimization loops and apply your findings to our pretraining, post-training and inference systems as applicable.

Drive product & research roadmap. Own our data roadmap end-to-end, formulating research questions, exploring/linking/expanding data sources and conducting experiments at your discretion. Your work will span data mining, machine learning, causal inference, survey design and more, and your results will be critical for decision-making in product development, research investment and overall business direction.

Build stable infrastructure. Your work will reach far beyond the jupyter kernel, manifesting in robust integrations with our research & product tech stacks, potentially in performance-critical paths. You'll also build large-scale standalone data processing systems, allocating resources as needed to manage the data ecosystem.

Champion scientific rigor. As our first quantitative researcher, you'll cultivate a culture of scientific rigor across the company and deepen common understanding of models, users and data. You'll proactively identify opportunities, define metrics, share results, and build a rigorous foundation upon which to understand our highly subjective domain.

We're looking for someone with deep quantitative expertise, preferably a Ph.D. in statistics, mathematics, physics, or another quantitative discipline, or 5+ years' industry experience as a quantitative analyst / data scientist. Autonomy & ownership are key, as you'll thrive in greenfield research domains, undefined product categories and small, flat teams. Engineering chops are also important, as you'll need to translate your ideas into clear, production-ready code and collaborate in an active research codebase.

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A successful candidate will be skilled in developing real-time object detection, sensor fusion, and state estimation algorithms using data from diverse mission sensors such as EO/IR cameras, radars, and IMUs. The role requires strong algorithmic thinking, deep familiarity with airborne sensing systems, and the ability to deliver performant software in simulation and real-world conditions.

Shield AI is committed to developing cutting-edge autonomy for unmanned aircraft operating across all Department of Defense (DoD) domains, including air, sea, and land. Our Perception Engineers are instrumental in creating the situational awareness that underpins autonomy, ensuring our systems understand and respond to the operational environment with speed, precision, and resilience.

Responsibilities:

• Lead teams across autonomy, integration, and testing by aligning technical efforts, resolving cross-functional challenges, and driving mission-focused execution.
• Develop advanced perception algorithms for object detection, classification, and multi-target tracking across diverse sensor modalities.
• Implement sensor fusion frameworks by integrating data from vision systems, radars, and other mission sensors using probabilistic and deterministic fusion techniques.
• Develop state estimation capabilities by designing and refining algorithms for localization and pose estimation using IMU, GPS, vision, and other onboard sensing inputs.
• Analyze and utilize sensor ICDs to ensure correct data handling, interpretation, and synchronization.
• Optimize perception performance by tuning and evaluating perception pipelines for performance, robustness, and real-time efficiency in both simulation and real-world environments.
• Support autonomy integration by working closely with autonomy, systems, and integration teams to interface perception outputs with planning, behaviors, and decision-making modules.
• Validate in simulated and operational settings by leveraging synthetic data, simulation environments, and field testing to validate algorithm accuracy and mission readiness.
• Collaborate with hardware and sensor teams to ensure seamless integration of perception algorithms with onboard compute platforms and diverse sensor payloads.
• Drive innovation in airborne sensing by contributing novel ideas and state-of-the-art techniques to advance real-time perception capabilities for unmanned aircraft operating in complex, GPS-denied, or contested environments.
• Travel Requirement – Members of this team typically travel around 10-15% of the year (to different office locations, customer sites, and flight integration events).

Requirements:

• BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience.
• Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 9 years and a Master’s degree; or 7 years with a PhD; or equivalent work experience.
• 7+ years of experience in Unmanned Systems programs in the DoD or applied R&D.
• 2+ years of people leadership experience.
• Background in implementing algorithms such as Kalman Filters, multi-target tracking, or deep learning-based detection models.
• Familiarity with fusing data from radar, EO/IR cameras, or other sensors using probabilistic or rule-based approaches.
• Familiarity with SLAM, visual-inertial odometry, or sensor-fused localization approaches in real-time applications.
• Ability to interpret and work with Interface Control Documents (ICDs) and hardware integration specs.
• Proficiency with version control, debugging, and test-driven development in cross-functional teams.
• Ability to obtain a SECRET clearance.

Preferences:

• Hands-on integration or algorithm development with airborne sensing systems.
• Experience with ML frameworks such as PyTorch or Tensorflow, particularly for vision-based object detection or classification tasks.
• Experience deploying perception software on SWaP-constrained platforms.
• Familiarity with validating perception systems during flight test events or operational environments.
• Understanding of sensing challenges in denied or degraded conditions.
• Exposure to perception applications across air, maritime, and ground platforms.

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A successful candidate will be skilled in developing real-time object detection, sensor fusion, and state estimation algorithms using data from diverse mission sensors such as EO/IR cameras, radars, and IMUs. The role requires strong algorithmic thinking, deep familiarity with airborne sensing systems, and the ability to deliver performant software in simulation and real-world conditions.

We are seeking a skilled and motivated manager to lead technical teams and support direct projects integrating perception solutions for defense platforms.

Shield AI is committed to developing cutting-edge autonomy for unmanned aircraft operating across all Department of Defense (DoD) domains, including air, sea, and land. Our Perception Engineers are instrumental in creating the situational awareness that underpins autonomy, ensuring our systems understand and respond to the operational environment with speed, precision, and resilience.

Responsibilities:

• Multidisciplinary Team Leadership – Lead teams across autonomy, integration, and testing by aligning technical efforts, resolving cross-functional challenges, and driving mission-focused execution.
• Develop advanced perception algorithms , Design and implement robust algorithms for object detection, classification, and multi-target tracking across diverse sensor modalities.
• Implement sensor fusion frameworks , Integrate data from vision systems, radars, and other mission sensors using probabilistic and deterministic fusion techniques to generate accurate situational awareness.
• Develop state estimation capabilities , Design and refine algorithms for localization and pose estimation using IMU, GPS, vision, and other onboard sensing inputs to enable stable and accurate navigation.
• Analyze and utilize sensor ICDs , Interpret interface control documents (ICDs) and technical specifications for aircraft-mounted sensors to ensure correct data handling, interpretation, and synchronization.
• Optimize perception performance , Tune and evaluate perception pipelines for performance, robustness, and real-time efficiency in both simulation and real-world environments.
• Support autonomy integration , Work closely with autonomy, systems, and integration teams to interface perception outputs with planning, behaviors, and decision-making modules.
• Validate in simulated and operational settings , Leverage synthetic data, simulation environments, and field testing to validate algorithm accuracy and mission readiness.
• Collaborate with hardware and sensor teams , Ensure seamless integration of perception algorithms with onboard compute platforms and diverse sensor payloads.
• Drive innovation in airborne sensing , Contribute novel ideas and state-of-the-art techniques to advance real-time perception capabilities for unmanned aircraft operating in complex, GPS-denied, or contested environments.
• Travel Requirement , Members of this team typically travel around 10-15% of the year (to different office locations, customer sites, and flight integration events).

Required Qualifications:

• BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience
• Typically requires a minimum of 7 years of related experience with a Bachelor’s degree; or 5 years and a Master’s degree; or 4 years with a PhD; or equivalent work experience
• 5+ years of experience in Unmanned Systems programs in the DoD or applied R&D
• 2+ years of people leadership experience
• Background in implementing algorithms such as Kalman Filters, multi-target tracking, or deep learning-based detection models.
• Familiarity with fusing data from radar, EO/IR cameras, or other sensors using probabilistic or rule-based approaches.
• Familiarity with SLAM, visual-inertial odometry, or sensor-fused localization approaches in real-time applications.
• Ability to interpret and work with Interface Control Documents (ICDs) and hardware integration specs.
• Proficiency with version control, debugging, and test-driven development in cross-functional teams.
• Ability to obtain a SECRET clearance.

Preferred Qualifications:

• Hands-on integration or algorithm development with airborne sensing systems.
• Experience with ML frameworks such as PyTorch or Tensorflow, particularly for vision-based object detection or classification tasks.
• Experience deploying perception software on SWaP-constrained platforms.
• Familiarity with validating perception systems during flight test events or operational environments.
• Understanding of sensing challenges in denied or degraded conditions.
• Exposure to perception applications across air, maritime, and ground platforms.

XML job scraping automation by YubHub

A successful candidate will be skilled in developing real-time object detection, sensor fusion, and state estimation algorithms using data from diverse mission sensors such as EO/IR cameras, radars, and IMUs. The role requires strong algorithmic thinking, deep familiarity with airborne sensing systems, and the ability to deliver performant software in simulation and real-world conditions.

Develop advanced perception algorithms , Design and implement robust algorithms for object detection, classification, and multi-target tracking across diverse sensor modalities. Implement sensor fusion frameworks , Integrate data from vision systems, radars, and other mission sensors using probabilistic and deterministic fusion techniques to generate accurate situational awareness. Develop state estimation capabilities , Design and refine algorithms for localization and pose estimation using IMU, GPS, vision, and other onboard sensing inputs to enable stable and accurate navigation. Analyze and utilize sensor ICDs , Interpret interface control documents (ICDs) and technical specifications for aircraft-mounted sensors to ensure correct data handling, interpretation, and synchronization. Optimize perception performance , Tune and evaluate perception pipelines for performance, robustness, and real-time efficiency in both simulation and real-world environments. Support autonomy integration , Work closely with autonomy, systems, and integration teams to interface perception outputs with planning, behaviors, and decision-making modules. Validate in simulated and operational settings , Leverage synthetic data, simulation environments, and field testing to validate algorithm accuracy and mission readiness. Collaborate with hardware and sensor teams , Ensure seamless integration of perception algorithms with onboard compute platforms and diverse sensor payloads. Drive innovation in airborne sensing , Contribute novel ideas and state-of-the-art techniques to advance real-time perception capabilities for unmanned aircraft operating in complex, GPS-denied, or contested environments.

XML job scraping automation by YubHub

A successful candidate will be skilled in developing real-time object detection, sensor fusion, and state estimation algorithms using data from diverse mission sensors such as EO/IR cameras, radars, and IMUs. The role requires strong algorithmic thinking, deep familiarity with airborne sensing systems, and the ability to deliver performant software in simulation and real-world conditions.

Shield AI is committed to developing cutting-edge autonomy for unmanned aircraft operating across all Department of Defense (DoD) domains, including air, sea, and land. Our Perception Engineers are instrumental in creating the situational awareness that underpins autonomy, ensuring our systems understand and respond to the operational environment with speed, precision, and resilience.

Responsibilities:

• Develop advanced perception algorithms , Design and implement robust algorithms for object detection, classification, and multi-target tracking across diverse sensor modalities.
• Implement sensor fusion frameworks , Integrate data from vision systems, radars, and other mission sensors using probabilistic and deterministic fusion techniques to generate accurate situational awareness.
• Develop state estimation capabilities , Design and refine algorithms for localization and pose estimation using IMU, GPS, vision, and other onboard sensing inputs to enable stable and accurate navigation.
• Analyze and utilize sensor ICDs , Interpret interface control documents (ICDs) and technical specifications for aircraft-mounted sensors to ensure correct data handling, interpretation, and synchronization.
• Optimize perception performance , Tune and evaluate perception pipelines for performance, robustness, and real-time efficiency in both simulation and real-world environments.
• Support autonomy integration , Work closely with autonomy, systems, and integration teams to interface perception outputs with planning, behaviors, and decision-making modules.
• Validate in simulated and operational settings , Leverage synthetic data, simulation environments, and field testing to validate algorithm accuracy and mission readiness.
• Collaborate with hardware and sensor teams , Ensure seamless integration of perception algorithms with onboard compute platforms and diverse sensor payloads.
• Drive innovation in airborne sensing , Contribute novel ideas and state-of-the-art techniques to advance real-time perception capabilities for unmanned aircraft operating in complex, GPS-denied, or contested environments.
• Travel Requirement , Members of this team typically travel around 10-15% of the year (to different office locations, customer sites, and flight integration events).

Requirements:

• BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience
• Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 9 years and a Master’s degree; or 7 years with a PhD; or equivalent work experience
• Background in implementing algorithms such as Kalman Filters, multi-target tracking, or deep learning-based detection models
• Familiarity with fusing data from radar, EO/IR cameras, or other sensors using probabilistic or rule-based approaches
• Familiarity with SLAM, visual-inertial odometry, or sensor-fused localization approaches in real-time applications
• Ability to interpret and work with Interface Control Documents (ICDs) and hardware integration specs
• Proficiency with version control, debugging, and test-driven development in cross-functional teams
• Ability to obtain a SECRET clearance

Preferences:

• Hands-on integration or algorithm development with airborne sensing systems
• Experience with ML frameworks such as PyTorch or Tensorflow, particularly for vision-based object detection or classification tasks
• Experience deploying perception software on SWaP-constrained platforms
• Familiarity with validating perception systems during flight test events or operational environments
• Understanding of sensing challenges in denied or degraded conditions
• Exposure to perception applications across air, maritime, and ground platforms

XML job scraping automation by YubHub

A successful candidate will be skilled in developing real-time object detection, sensor fusion, and state estimation algorithms using data from diverse mission sensors such as EO/IR cameras, radars, and IMUs. The role requires strong algorithmic thinking, deep familiarity with airborne sensing systems, and the ability to deliver performant software in simulation and real-world conditions.

Shield AI is committed to developing cutting-edge autonomy for unmanned aircraft operating across all Department of Defense (DoD) domains, including air, sea, and land. Our Perception Engineers are instrumental in creating the situational awareness that underpins autonomy, ensuring our systems understand and respond to the operational environment with speed, precision, and resilience.

Responsibilities:

• Develop advanced perception algorithms , Design and implement robust algorithms for object detection, classification, and multi-target tracking across diverse sensor modalities.
• Implement sensor fusion frameworks , Integrate data from vision systems, radars, and other mission sensors using probabilistic and deterministic fusion techniques to generate accurate situational awareness.
• Develop state estimation capabilities , Design and refine algorithms for localization and pose estimation using IMU, GPS, vision, and other onboard sensing inputs to enable stable and accurate navigation.
• Analyze and utilize sensor ICDs , Interpret interface control documents (ICDs) and technical specifications for aircraft-mounted sensors to ensure correct data handling, interpretation, and synchronization.
• Optimize perception performance , Tune and evaluate perception pipelines for performance, robustness, and real-time efficiency in both simulation and real-world environments.
• Support autonomy integration , Work closely with autonomy, systems, and integration teams to interface perception outputs with planning, behaviors, and decision-making modules.
• Validate in simulated and operational settings , Leverage synthetic data, simulation environments, and field testing to validate algorithm accuracy and mission readiness.
• Collaborate with hardware and sensor teams , Ensure seamless integration of perception algorithms with onboard compute platforms and diverse sensor payloads.
• Drive innovation in airborne sensing , Contribute novel ideas and state-of-the-art techniques to advance real-time perception capabilities for unmanned aircraft operating in complex, GPS-denied, or contested environments.
• Travel Requirement , Members of this team typically travel around 10-15% of the year (to different office locations, customer sites, and flight integration events).

Required Qualifications:

• BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience
• Typically requires a minimum of 7 years of related experience with a Bachelor’s degree; or 5 years and a Master’s degree; or 4 years with a PhD; or equivalent work experience
• Background in implementing algorithms such as Kalman Filters, multi-target tracking, or deep learning-based detection models
• Familiarity with fusing data from radar, EO/IR cameras, or other sensors using probabilistic or rule-based approaches
• Familiarity with SLAM, visual-inertial odometry, or sensor-fused localization approaches in real-time applications
• Ability to interpret and work with Interface Control Documents (ICDs) and hardware integration specs
• Proficiency with version control, debugging, and test-driven development in cross-functional teams
• Ability to obtain a SECRET clearance

Preferred Qualifications:

• Hands-on integration or algorithm development with airborne sensing systems
• Experience with ML frameworks such as PyTorch or Tensorflow, particularly for vision-based object detection or classification tasks
• Experience deploying perception software on SWaP-constrained platforms
• Familiarity with validating perception systems during flight test events or operational environments
• Understanding of sensing challenges in denied or degraded conditions
• Exposure to perception applications across air, maritime, and ground platforms

$182,720 - $274,080 a year

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When new product ideas are identified, our software engineers are responsible for designing, developing, testing, and deploying the products from the ground up, while continuously iterating with product management and customers for feedback and input.

This position follows the Company’s hybrid schedule which currently requires employees to work in the Durham, NC or Addison, TX office a minimum of three days per week.

Responsibilities:

• Design, develop, and deploy AI Agents and machine learning models to solve complex business problems
• Create and maintain backend systems for LLMs and AI Agents, ensuring high performance and scalability
• Collaborate with cross-functional teams to integrate AI solutions into existing systems and workflows
• Optimise and fine-tune models and AI Agents for performance, accuracy, and efficiency
• Implement and manage CI/CD pipelines for AI model and AI Agent deployment
• Monitor and troubleshoot AI systems in production, ensuring reliability and robustness
• Stay updated with the latest advancements in AI, machine learning, AI Agents, and related technologies

Requirements:

• Education: Bachelor's degree or equivalent experience grown
• CS Degree preferred
• Experience: 3+ years of professional experience
• Proven experience as an AI Developer, AI Engineer, or similar role
• Expert knowledge of at least one of Golang (preferred), Python or Java and experience with Kubernetes
• Strong understanding of machine learning algorithms and frameworks (e.g., TensorFlow, PyTorch)
• Experience with LLMs, AI Agents, and natural language processing (NLP) techniques
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and containerisation technologies
• Demonstrates sound judgment for balancing between rapid development, and long-term code maintainability and supportability
• Communicates effectively, both technically and non-technically, as well as in written and oral forms
• Believes in the power of and the need for writing automated tests as part of development
• Skilled debugger who can put out fires under pressure when things go wrong in production environments
• Has knowledge of a variety of modern software frameworks (server side & browser side) and the versatility to learn new tools
• Detail-oriented and passionate for creating an awesome user experience

Benefits:

• Annual base salary range: $100,000 - $295,000
• Equity awards
• Generous benefits package consisting of medical, dental and vision plan, family planning benefits, health savings account, flexible spending account, transportation savings account, 401(k) retirement savings plan with company match, life, accident and disability coverage, business travel accident insurance, employee assistance programs, disability insurance, and other well-being benefits

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You will work closely with cross-functional teams to ensure the seamless integration of software components into our ASV platform.

Responsibilities:

• Collaborate with hardware engineers, robotics engineers, and other software engineers across the tech stack to design, develop, and deploy software solutions for autonomous surface vessels
• Participate in all phases of the software development lifecycle, including requirements gathering, design, implementation, testing, deployment, and maintenance
• Develop robust, scalable, and maintainable software systems that meet the unique challenges of autonomous maritime operations
• Implement algorithms for perception, navigation, path planning, and control to enable autonomous behavior in ASVs
• Optimise software performance and reliability to meet stringent DoD requirements and operational standards
• Conduct thorough testing and validation of software components to ensure functionality, accuracy, and safety
• Stay current with emerging technologies and industry trends in autonomous systems, robotics, and maritime technology

Qualifications:

• Bachelor's degree in Computer Science, Software Engineering, or a related field
• Proven experience in software development, with a focus on autonomous systems, robotics, or related fields
• Proficiency in programming languages such as C++, Python, or Java, with a strong emphasis on object-oriented design and development
• Experience with software development tools and frameworks commonly used in robotics and autonomous systems (e.g., ROS, OpenCV, TensorFlow, etc.)
• Familiarity with sensor fusion techniques, SLAM algorithms, and other technologies relevant to autonomous navigation and perception
• Strong problem-solving skills and the ability to work effectively in a fast-paced environment
• Excellent communication skills and the ability to clearly articulate technical concepts

Benefits:

• Medical Insurance: Comprehensive health insurance plans covering a range of services
• Dental and Vision Insurance: Coverage for routine dental check-ups, orthodontics, and vision care
• Saronic pays 100% of the premium for employees and 80% for dependents
• Time Off: Generous PTO and Holidays
• Parental Leave: Paid maternity and paternity leave to support new parents
• Competitive Salary: Industry-standard salaries with opportunities for performance-based bonuses
• Retirement Plan: 401(k) plan
• Stock Options: Equity options to give employees a stake in the company’s success
• Life and Disability Insurance: Basic life insurance and short- and long-term disability coverage
• Additional Perks: Free lunch benefit and unlimited free drinks and snacks in the office

Physical Demands:

• Prolonged periods of sitting at a desk and working on a computer.
• Occasional standing and walking within the office.
• Manual dexterity to operate a computer keyboard, mouse, and other office equipment.
• Visual acuity to read screens, documents, and reports.
• Occasional reaching, bending, or stooping to access file drawers, cabinets, or office supplies.
• Lifting and carrying items up to 20 pounds occasionally (e.g., office supplies, packages).

Additional Information:

This role requires access to export-controlled information or items that require “U.S. Person” status. As defined by U.S. law, individuals who are any one of the following are considered to be a “U.S. Person”: (1) U.S. citizens, (2) legal permanent residents (a.k.a. green card holders), and (3) certain protected classes of asylees and refugees, as defined in 8 U.S.C. 1324b(a)(3).

XML job scraping automation by YubHub

Our team is focused on making boats go and perform tasks with no human involvement. This job is available at multiple levels, including entry, senior, and staff.

The successful candidate will develop algorithms and models which allow boats to sense and navigate, as well as develop metrics which allow quantitative analysis of improvements and regressions in boat performance.

Responsibilities:

• Develop algorithms and models which allow boats to sense and navigate
• Develop metrics which allow quantitative analysis of improvements and regressions in boat performance
• Analyze and work with large data systems to enable model training and evaluation

Requirements:

• Strong programming fundamentals
• Extensive programming experience and demonstrated ability to work on large systems
• Computing Fundamentals
• A general understanding of operating systems and or similar large scale systems
• An understanding of basic computer architecture
• A demonstrated willingness to learn and pivot based on new information

Useful Skills:

• Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch)
• Understanding of various filters and their applications
• Proficiency in Rust
• Experience with maritime or autonomous vehicle projects
• Experience with signals processing or sensor fusion
• Experience with low latency inference and tracking pipelines
• Experience with path planning algorithms
• Experience training and deploying multi modal models
• Experience with various sensors including radar, cameras, and lidar
• Experience developing and optimizing deployed ML systems

Physical Demands:

• Prolonged periods of sitting at a desk and working on a computer
• Occasional standing and walking within the office
• Manual dexterity to operate a computer keyboard, mouse, and other office equipment
• Visual acuity to read screens, documents, and reports
• Occasional reaching, bending, or stooping to access file drawers, cabinets, or office supplies
• Lifting and carrying items up to 20 pounds occasionally (e.g., office supplies, packages)

Benefits:

• Medical Insurance: Comprehensive health insurance plans covering a range of services
• Dental and Vision Insurance: Coverage for routine dental check-ups, orthodontics, and vision care
• Saronic pays 100% of the premium for employees and 80% for dependents
• Time Off: Generous PTO and Holidays
• Parental Leave: Paid maternity and paternity leave to support new parents
• Competitive Salary: Industry-standard salaries with opportunities for performance-based bonuses
• Retirement Plan: 401(k) plan
• Stock Options: Equity options to give employees a stake in the company’s success
• Life and Disability Insurance: Basic life insurance and short- and long-term disability coverage
• Additional Perks: Free lunch benefit and unlimited free drinks and snacks in the office

Additional Information:

This role requires access to export-controlled information or items that require “U.S. Person” status. As defined by U.S. law, individuals who are any one of the following are considered to be a “U.S. Person”: (1) U.S. citizens, (2) legal permanent residents (a.k.a. green card holders), and (3) certain protected classes of asylees and refugees, as defined in 8 U.S.C. 1324b(a)(3).

XML job scraping automation by YubHub

Mistral AI is a pioneering company shaping the future of AI. They believe in the power of AI to simplify tasks, save time, and enhance learning and creativity.

Role Summary

The Research Engineering team at Mistral AI spans Platform (shared infra & clean code) and Embedded (inside research squads). Engineers can move along the research↔production spectrum as needs or interests evolve. As a Research Engineer – ML track, you’ll build and optimise the large-scale learning systems that power their open-weight models.

Responsibilities

• Accelerate researchers by taking on the heavy parts of large-scale ML pipelines and building robust tools.
• Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs.
• Conduct experiments on the latest deep-learning techniques (sparsified 70 B + runs, distributed training on thousands of GPUs).
• Design, implement and benchmark ML algorithms; write clear, efficient code in Python.
• Deliver prototypes that become production-grade components for Le Chat and their enterprise API.

Requirements

• Master’s or PhD in Computer Science (or equivalent proven track record).
• 4 + years working on large-scale ML codebases.
• Hands-on with PyTorch, JAX or TensorFlow; comfortable with distributed training (DeepSpeed / FSDP / SLURM / K8s).
• Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops.
• Strong software-design instincts: testing, code review, CI/CD.
• Self-starter, low-ego, collaborative.

What we offer

• Competitive salary and equity.
• Healthcare: Medical/Dental/Vision covered for you and your family.
• Pension: 401K (6% matching)
• PTO: 18 days
• Transportation: Reimburse office parking charges, or $120/month for public transport
• Sport: $120/month reimbursement for gym membership
• Meal stipend: $400 monthly allowance for meals (solution might evolve as they grow bigger)
• Visa sponsorship
• Coaching: they offer BetterUp coaching on a voluntary basis

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We are seeking a Research Engineer to join our Machine Learning team. As a Research Engineer, you will work on building and optimizing large-scale learning systems that power our open-weight models.

Responsibilities

• Accelerate researchers by taking on the heavy parts of large-scale ML pipelines and building robust tools.
• Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs.
• Conduct experiments on the latest deep-learning techniques.
• Design, implement and benchmark ML algorithms; write clear, efficient code in Python.
• Deliver prototypes that become production-grade components for Le Chat and our enterprise API.

Requirements

• Master's or PhD in Computer Science (or equivalent proven track record).
• 4 + years working on large-scale ML codebases.
• Hands-on with PyTorch, JAX or TensorFlow; comfortable with distributed training (DeepSpeed / FSDP / SLURM / K8s).
• Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops.
• Strong software-design instincts: testing, code review, CI/CD.
• Self-starter, low-ego, collaborative.

Benefits

• Competitive cash salary and equity.
• Food: Daily lunch vouchers.
• Sport: Monthly contribution to a Gympass subscription.
• Transportation: Monthly contribution to a mobility pass.
• Health: Full health insurance for you and your family.
• Parental: Generous parental leave policy.

Note: Benefits may vary depending on location.

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They will be responsible for the development of algorithms for early, blood-based detection tests for cancer. They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organisation dedicated to changing the entire landscape of cancer.

The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.

Responsibilities:

• Independently pursue cutting-edge research in AI applied to biological problems (including cancer research, genomics, computational biology, immunology, etc.)
• Build new models or fine-tune existing models to identify biological changes resulting from disease
• Build models that achieve high accuracy and that generalise robustly to new data
• Apply contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms
• Work closely with ML Engineering partners to ensure that Freenome's computational infrastructure supports optimal model training and iteration
• Take a mindful, transparent, and humane approach to your work

Requirements:

• PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics
• 6+ years of post-doc or post-PhD industry experience achieving impactful results using relevant modelling techniques
• Expertise demonstrated by research publications or industry achievements, in driving independent research in applied machine learning, deep learning and complex data modelling
• Practical and theoretical understanding of fundamental ML models like generalised linear models, kernel machines, decision trees and forests, neural networks, boosting and model aggregation
• Practical and theoretical understanding of DL models like large language models or other foundation models
• Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning
• Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data
• Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.
• Proficiency in one or more ML frameworks such as; PyTorch, TensorFlow and JAX; and ML platforms like Hugging Face
• Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights & Biases
• Excellent ability to communicate across disciplines, work collaboratively, and make progress in smaller steps via experimental iterations
• Proficient at productive cross-functional scientific communication and collaboration with software engineers and computational biologists
• A passion for innovation and demonstrated initiative in tackling new areas of research

Nice to have:

• Deep domain-specific experience in computational biology, genomics, proteomics or a related field
• Experience in building DL models for genomic data, with knowledge of state-of-the-art DNA foundation models
• Experience in NGS data analysis and bioinformatic pipelines
• Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS
• Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems

Benefits and additional information:

• The US target range of our base salary for new hires is $199,675.00 - $283,500.00. You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered. Please note that individual total compensation for this position will be determined at the Company's sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education.
• Freenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.
• Applicants have rights under Federal Employment Laws.

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The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.

Key responsibilities include:

• Independently pursuing cutting-edge research in AI applied to biological problems
• Building new models or fine-tuning existing models to identify biological changes resulting from disease
• Building models that achieve high accuracy and that generalise robustly to new data
• Applying contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms
• Working closely with ML Engineering partners to ensure that Freenome's computational infrastructure supports optimal model training and iteration
• Taking a mindful, transparent, and humane approach to your work

Requirements include:

• PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics
• 3+ years of postdoc or post-PhD industry experience achieving impactful results using relevant modelling techniques
• Expertise, demonstrated by research publications or industry achievements, in applied machine learning, deep learning and complex data modelling
• Practical and theoretical understanding of fundamental ML models like generalised linear models, kernel machines, decision trees and forests, neural networks
• Practical and theoretical understanding of DL models like large language models or other foundation models
• Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning
• Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data
• Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.
• Proficiency in one or more ML frameworks such as; Pytorch, Tensorflow and Jax; and ML platforms like Hugging Face
• Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights & Biases
• Excellent ability to communicate across disciplines, work collaboratively, and make progress in smaller steps via experimental iterations
• A passion for innovation and demonstrated initiative in tackling new areas of research

Nice to have qualifications include:

• Deep domain-specific experience in computational biology, genomics, proteomics or a related field
• Experience in building DL models for genomic data, with knowledge of state-of-the-art DNA foundation models
• Experience in NGS data analysis and bioinformatic pipelines
• Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS
• Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems

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The MLS team at Freenome develops DL models using massive-scale genomic data that presents significant challenges for current training paradigms. The Senior Machine Learning Research Engineer will primarily be responsible for developing and deploying the infrastructure needed to support development of such DL models: enabling distributed DL pipelines, optimising hardware utilisation for efficient training, and performing model optimisations.

As part of an interdisciplinary R&D team, they will work in close collaboration with machine learning scientists, computational biologists and software engineers to accelerate the development of state-of-the-art ML/AI models and help Freenome achieve its mission.

Key responsibilities include:

• Implementing and refining DL pipelines on distributed computing platforms to enhance the speed and efficiency of DL operations, including model training, data handling, model management, and inference.
• Collaborating closely with ML scientists and software engineers to understand current challenges and requirements and ensure that the DL model development pipelines created are perfectly aligned with scientific goals and operational needs.
• Continuously monitoring, evaluating, and optimising DL model training pipelines for performance and scalability.
• Staying up to date with the latest advancements in AI, ML, and related technologies, and quickly learning and adapting new tools and frameworks, if necessary.
• Developing and maintaining robust and reproducible DL pipelines that guarantee that DL pipelines can be reliably executed, maintaining consistency and accuracy of results.
• Driving performance improvements across our stack through profiling, optimisation, and benchmarking. Implementing efficient caching solutions and debugging distributed systems to accelerate both training and evaluation pipelines.
• Acting as a bridge facilitating communication between the engineering and scientific teams, documenting and sharing best practices to foster a culture of learning and continuous improvement.

Must-haves include:

• MS or equivalent experience in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Software Engineering, with an emphasis on AI/ML theory and/or practical development.
• 5+ years of post-MS industry experience working on developing AI/ML software engineering pipelines.
• Proficiency in a general-purpose programming language: Python (preferred), Java, Julia, C, C++, etc.
• Strong knowledge of ML and DL fundamentals and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Jax or Scikit-learn.
• In-depth knowledge of scalable and distributed computing platforms that support complex model training (such as Ray or DeepSpeed) and their integration with ML developer tools like TensorBoard, Wandb, or MLflow.
• Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and how to deploy and manage AI/ML models and pipelines in a cloud environment.
• Understanding of containerisation technologies (e.g., Docker) and computing resource orchestration tools (e.g., Kubernetes) for deploying scalable ML/AI solutions.
• Proven track record of developing and optimising workflows for training DL models, large language models (LLMs), or similar for problems with high data complexity and volume.
• Experience managing large datasets, including data storage (such as HDFS or Parquet on S3), retrieval, and efficient data processing techniques (via libraries and executors such as PyArrow and Spark).
• Proficiency in version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) practices to maintain code quality and automate development workflows.
• Expertise in building and launching large-scale ML frameworks in a scientific environment that supports the needs of a research team.
• Excellent ability to work effectively with cross-functional teams and communicate across disciplines.

Nice-to-haves include:

• Experience working with large-scale genomics or biological datasets.
• Experience managing multimodal datasets, such as combinations of sequence, text, image, and other data.
• Experience GPU/Accelerator programming and kernel development (such as CUDA, Triton or XLA).
• Experience with infrastructure-as-code and configuration management.
• Experience cultivating MLOps and ML infrastructure best practices, especially around reliability, provisioning and monitoring.
• Strong track record of contributions to relevant DL projects, e.g. on github.

The US target range of our base salary for new hires is $161,925 - $227,325. You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered.

Freenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, colour, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.

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At Cresta, we are on a mission to turn every customer conversation into a competitive advantage by unlocking the true potential of the contact center. Our platform combines the best of AI and human intelligence to help contact centers discover customer insights and behavioural best practices, automate conversations and inefficient processes, and empower every team member to work smarter and faster.

As a Senior Machine Learning Engineer, you will lead the design and development of Cresta's next-generation AI Agents and Agentic Assist systems, defining system architecture and core modeling approaches. You will architect intelligent, multi-step agent workflows that combine real-time guidance, knowledge retrieval, reasoning, summarization, and automated actions into cohesive production systems.

Responsibilities:

• Lead the design and development of Cresta's next-generation AI Agents and Agentic Assist systems, defining system architecture and core modeling approaches.
• Architect intelligent, multi-step agent workflows that combine real-time guidance, knowledge retrieval, reasoning, summarization, and automated actions into cohesive production systems.
• Design, deploy, and optimize LLM-powered systems, including Retrieval-Augmented Generation (RAG) pipelines, multi-agent orchestration, and domain-adapted models.
• Improve reasoning, planning, and tool-use capabilities in real-world AI applications.
• Develop evaluation strategies for complex, non-deterministic systems, including offline benchmarking, online experimentation, and LLM-as-a-judge methodologies.
• Diagnose and mitigate real-world failure modes such as hallucinations, retrieval errors, tool misuse, prompt brittleness, and multi-step reasoning breakdowns.
• Define and measure quality metrics (e.g., accuracy, faithfulness, task completion, latency, cost, robustness) to improve system reliability and performance.
• Optimize AI systems for scalability, latency, security, and cost efficiency in production environments.
• Collaborate cross-functionally with product, frontend, and backend teams to integrate AI capabilities seamlessly into Cresta's platform.
• Mentor engineers, contribute to technical strategy, and help shape the roadmap for Cresta's AI systems.

Qualifications:

• Bachelor's degree in Computer Science, Mathematics, or a related field; Master's or Ph.D. preferred.
• 5–8+ years of industry experience building and deploying machine learning systems in production, including significant experience working with LLMs.
• Strong expertise in NLP, Generative AI, transformer architectures, embeddings, and retrieval systems.
• Proven experience designing and deploying Retrieval-Augmented Generation (RAG) systems in enterprise environments.
• Experience building and evaluating complex agentic or multi-step LLM workflows.
• Strong knowledge of modern ML frameworks and tools (e.g., PyTorch, TensorFlow, Hugging Face) and distributed/cloud-based infrastructure.
• Demonstrated ability to optimize real-time ML systems for performance, scalability, and reliability.
• Strong technical leadership skills, with the ability to influence cross-functional decisions and raise the engineering bar.

Perks & Benefits:

• We offer Cresta employees a variety of medical, dental, and vision plans, designed to fit you and your family's needs.
• Paid parental leave to support you and your family.
• Monthly Health & Wellness allowance.
• Work from home office stipend to help you succeed in a remote environment.
• Lunch reimbursement for in-office employees.
• PTO: 3 weeks in Canada.

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Responsibilities

• Define the long-term vision and roadmap for Alluxio's AI data platform, covering inference, training, and agentic workloads.
• Collaborate with engineering to design features that deliver high-throughput, low-latency data access (e.g., GPU-aware caching, streaming reads, tiered prefetching).
• Ensure seamless integration with frameworks like PyTorch, TensorFlow, Ray, and Triton; evolve Alluxio's APIs for AI-native workloads.
• Engage directly with enterprise AI teams to understand workload patterns, validate impact, and prioritize roadmap direction.
• Stay ahead of trends in multi-model serving, retrieval-augmented generation (RAG), and agentic orchestration; translate them into actionable product plans.

Requirements

• 5–9 years of experience in product management or technical leadership within AI infrastructure, ML platforms, or distributed systems.
• Strong understanding of AI/ML workflows , from model training and deployment to inference and data-access pipelines.
• Proven track record of delivering infrastructure features that improve latency, GPU utilization, or total cost of ownership.
• Technical fluency with distributed systems, caching, and cloud orchestration (Kubernetes, AWS/GCP/Azure).
• Familiarity with AI frameworks such as PyTorch, TensorFlow, Triton, Ray, or LangChain.
• Exceptional communication and strategic thinking , ability to translate complex systems work into clear, prioritized roadmaps.

Benefits

• Shape how the world's most advanced AI systems access and process data.
• Work at the intersection of distributed systems, AI acceleration, and open source.
• Collaborate with world-class engineers, researchers, and customers driving the AI frontier.
• Competitive compensation and equity package with comprehensive benefits.
• A culture built on curiosity, empathy, and deep technical rigor.

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They work on assignments like designing, developing, and troubleshooting software, leveraging the state-of-the-art technologies like AI/ML, GenAI and Cloud. Their critical contributions enable world-wide EDA designers to extend the frontiers of semiconductors and chip development.

Job Description

Date posted: 03/29/2026

We Are:

At Synopsys, we drive the innovations that shape the way we live and connect. Our technology is central to the Era of Pervasive Intelligence, from self-driving cars to learning machines. We lead in chip design, verification, and IP integration, empowering the creation of high-performance silicon chips and software content. Join us to transform the future through continuous technological innovation.

You Are:

You are a forward-thinking engineer with a passion for leveraging cutting-edge AI technologies to revolutionize electronic design automation and verification. You thrive in dynamic environments where innovation, collaboration, and continuous learning are valued. Your experience in verification frontend flows and AI/ML frameworks enables you to bridge the gap between traditional engineering practices and intelligent automation. You are comfortable working across diverse teams, collaborating with design, verification, CAD, and methodology experts to identify impactful automation opportunities. You possess strong analytical skills, enabling you to dissect complex verification challenges and develop scalable GenAI solutions. Your commitment to professional growth is evident in your eagerness to stay current with the latest advancements in LLMs, GenAI, and verification technology. With a keen eye for detail and a drive to deliver high-quality results, you are adept at integrating AI-driven capabilities into established workflows, elevating productivity and efficiency. You value inclusivity and diverse perspectives, and you are motivated by the opportunity to shape the future of engineering through innovative, intelligent solutions.

What You’ll Be Doing:

• Develop and deploy LLM/GenAI-based solutions to enhance verification productivity across static, formal, and simulation-based flows in EDA tools.

• Collaborate cross-functionally with design, verification, CAD, and methodology teams to identify high-impact areas for AI-assisted automation.

• Build tools and frameworks to generate or refine assertions, constraints, checkers, and test intent, summarize design/spec content, and analyze logs, failures, and coverage gaps.

• Integrate LLM-driven capabilities into existing verification flows, tools, and automation infrastructure, ensuring seamless adoption.

• Develop and maintain scripts, data pipelines, and evaluation frameworks for AI-assisted verification use cases.

• Stay current with advances in LLMs, GenAI, verification technology, and digital design methodologies to inform best practices.

• Participate in technical reviews and help define scalable AI adoption strategies within verification environments.

The Impact You Will Have:

• Accelerate verification planning, setup, and closure, enabling faster time-to-market for complex digital designs.

• Enhance productivity and efficiency for engineering teams through intelligent automation and AI-driven solutions.

• Reduce manual effort and potential errors in verification by automating routine and complex tasks.

• Improve coverage analysis, debug processes, and testbench/content generation, resulting in higher quality silicon chips.

• Drive innovation in verification methodologies by integrating state-of-the-art GenAI capabilities.

• Foster cross-functional collaboration, contributing to robust and scalable verification strategies.

• Support Synopsys’ leadership in EDA technology and AI-driven engineering solutions.

What You’ll Need:

• Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, Computer Science, or related field.

• 5–8 years of experience in EDA software development with prior experience in developing GenAI-based tools.

• Hands-on experience with LLM/GenAI or AI/ML frameworks/tools such as PyTorch, TensorFlow, Hugging Face, LangChain, or equivalent.

• Proficiency in C++ and familiarity with Verilog, VHDL, or SystemVerilog.

• Strong understanding of verification frontend methodologies, including static analysis (CDC/RDC/Lint), formal/property-based verification, and simulation bring-up/debug.

• Experience with cloud or scalable compute platforms (AWS, GCP, Azure) is a plus.

• Familiarity with Agile development methodologies is desirable.

Who You Are:

• Innovative thinker with a passion for applying AI to real-world engineering challenges.

• Effective communicator, able to convey complex technical concepts to diverse audiences.

• Collaborative team player who thrives in cross-functional environments.

• Strong problem-solving abilities and analytical mindset.

• Adaptable, eager to learn, and comfortable with ambiguity in fast-evolving technology landscapes.

• Self-driven and proactive, with a commitment to delivering impactful solutions.

The Team You’ll Be A Part Of:

You will join a collaborative and innovative engineering team focused on advancing verification productivity through AI-driven solutions. The team works closely with design, verification, CAD, and methodology groups to identify and implement high-impact automation strategies. Together, you will drive the adoption of GenAI technologies within Synopsys’ EDA ecosystem, fostering a culture of continuous improvement and technological excellence.

Rewards and Benefits:

We offer a comprehensive range of health, wellness, and financial benefits to cater to your needs. Our total rewards include both monetary and non-monetary offerings. Your recruiter will provide more details about the salary range and benefits during the hiring process.

At Synopsys, we want talented people of every background to feel valued and supported to do their best work. Synopsys considers all applicants for employment without regard to race, color, religion, national origin, gender, sexual orientation, age, military veteran status, or disability.

Benefits

At Synopsys, innovation is driven by our incredible team around the world. We feel honored to work alongside such talented and passionate individuals who choose to make a difference here every day. We're proud to provide the comprehensive benefits and rewards that our team truly deserves.

Visit Benefits Page

• ### Health & Wellness

Comprehensive medical and healthcare plans that work for you and your family.

• ### Time Away

In addition to company holidays, we have ETO and FTO Programs.

• ### Family Support

Maternity and paternity leave, parenting resources, adoption and surrogacy assistance, and more.

• ### ESPP

Purchase Synopsys common stock at a 15% discount, with a 24 month look-back.

• ### Retirement Plans

Save for your future with our retirement plans that vary by region and country.

• ### Compensation

Competitive salaries.

\\ Benefits vary by country and region - check with your recruiter to confirm

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You will design, develop, and optimize high-performance software applications using C/C++. You will build and maintain large-scale software systems, focusing on scalability, reliability, and performance. You will collaborate with cross-functional teams to gather requirements and prioritize project deliverables.

You will participate in code reviews, promoting best practices, and contributing to codebase improvements. You will troubleshoot and debug complex software issues, ensuring robust and efficient solutions. You will stay current with industry trends and emerging technologies, integrating them into development efforts.

As a member of the team, you will drive the development of innovative software applications that empower next-generation chip design and intelligent systems. You will ensure the scalability and reliability of mission-critical software used by leading semiconductor companies worldwide.

You will elevate product quality and performance through rigorous code reviews and technical mentorship. You will enhance Synopsys' competitive edge by integrating cutting-edge technologies and methodologies into our products ΔΗΜ

The ideal candidate will have expert-level proficiency in C/C++ programming, with a strong grasp of object-oriented design and optimization techniques. You will have 5 to 10 years of experience in similar role in EDA domain, preferably in developing Simulation tools.

You will have a solid foundation in computer science including data structures, algorithms, and software design patterns. You will have experience developing and maintaining large-scale, high-performance software systems.

You will be able to troubleshoot, debug, and resolve complex software issues efficiently. Familiarity with compiler design and implementation is highly desirable. Experience with AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch) is a plus.

You will be an analytical thinker with strong problem-solving skills. You will be an effective communicator and collaborator, comfortable working across diverse teams. You will be detail-oriented and committed to delivering high-quality software solutions.

You will be a proactive learner, eager to stay ahead of industry trends and technologies. You will be adaptable and resilient, thriving in fast-paced and evolving environments. You will be a mentor and team player, willing to share knowledge and support peers.

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We are seeking a highly motivated and innovative Research Scientist to join our team in Bangalore, focused on advancing the state-of-the-art in autonomous agents through reinforcement learning and ML optimization methods.

As a Research Scientist at Google DeepMind, you will conduct cutting-edge research on large language models (LLMs), focusing on the development of more capable, robust autonomous agents.

Key Responsibilities:

• Design, implement and evaluate models, agents and software prototypes of large foundational models.
• Push the boundary of state of the art RL methods and machine learning optimization methods to build autonomous agents.
• Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.
• Suggest and engage in team collaborations to meet ambitious research goals.
• Work with external collaborators and maintain relationships with relevant research labs and key individuals as appropriate.
• Work in collaboration with our Responsible AI teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

About You:

• You are a passionate and talented researcher with a strong foundation and a proven ability to conduct impactful research in AI.
• You have a collaborative mindset and are excited to work as part of a team to tackle ambitious research challenges.
• You are passionate about seeing your research translated into real-world products that improve the lives of users and are eager to work in an environment where research has a direct path to product impact.

Requirements:

• PhD in Computer Science, Artificial Intelligence, or a related field.
• Strong publication record in top-tier machine learning conferences or journals.
• Solid understanding of deep learning, natural language processing, computer vision, and/or speech processing.
• Experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch.

Preferred Qualifications:

• Experience with multimodal learning, large language models, and/or assistive AI agents.
• Experience with prompt engineering, few-shot learning, post-training techniques, and evaluations.
• Familiarity with large-scale model training and deployment.
• Strong programming skills in Python or similar languages.
• Excellent communication and collaboration skills.

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Our Minesweeper squad produces Human Understandable Language Knowledge to enrich music and talk content understanding. We use AI and ML techniques, including Large Language Models, to understand music, podcasts and audiobooks, building reliable, scalable systems to distribute that knowledge to Spotify internal teams, users, and creators.

We are looking for a Machine Learning Engineer to join our team and help build the future of music, podcast and audiobook listening experiences for millions of listeners at Spotify. This is a unique opportunity to help develop and shape Spotify content enrichment, and recommendations.

As a Machine Learning Engineer, you will:

• Utilize in-house and 3rd party LLMs to solve language understanding problems
• Employ techniques such as fine-tuning and RAG to improve models
• Contribute to designing, building, evaluating, shipping, and refining Spotify’s product by hands-on ML development
• Help drive optimization, testing, and tooling to improve quality of our content enrichment assets
• Collaborate with cross-functional teams of MLEs, data and backend engineers, and other stakeholders including tech research, data science, and product to develop new features and technologies
• Perform data analysis to establish baselines and inform product decisions
• Stay up-to-date on the latest machine learning algorithms and techniques

You will be part of a motivated and supportive team that values agile software processes, data-driven development, reliability, and disciplined experimentation.

If you have a strong background in machine learning, especially experience with Large Language Models, and are passionate about fostering collaborative teams, we encourage you to apply.

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At Google DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. Our special interdisciplinary team combines the best techniques from deep learning, reinforcement learning, and systems neuroscience to build general-purpose learning algorithms.

Research Scientists lead our efforts in developing novel tools, infrastructure, and algorithms towards the end goal of solving and building Artificial General Intelligence.

unparalleled opportunities to work with a talented team of researchers and engineers.

Drawing on expertise from a variety of disciplines including deep learning, computer vision, language modeling, and advanced generative architectures, our Research Scientists are at the forefront of groundbreaking research.

Responsibilities:

• Design, rapidly implement, and rigorously evaluate cutting-edge deep learning algorithms and data curation for multimodal generative AI, with a particular emphasis on culturally-adapted image and video synthesis.
• Report and present research findings and developments clearly and efficiently both internally and externally, verbally and in writing.
• Suggest and engage in team collaborations to meet ambitious research goals, while also driving significant individual contributions.
• Work in collaboration with our Ethics and Governance teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

Requirements:

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or equivalent practical experience.
• 2+ years of relevant experience in deep learning research and development, particularly in generative AI and related to image and video synthesis. This includes diffusion models and autoregressive generative models.
• Experience in software development with one or more programming languages (e.g., Python) and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch), with a track record of building high-quality research prototypes and systems.

Preferred Qualifications:

• Demonstrated experience in large-scale training of multimodal generative models.
• A track record of research or engineering achievements, including publications in peer-reviewed conferences or journals.

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At Google DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. Our special interdisciplinary team combines the best techniques from deep learning, reinforcement learning, and systems neuroscience to build general-purpose learning algorithms.

Research Scientists lead our efforts in developing novel tools, infrastructure, and algorithms towards the end goal of solving and building Artificial General Intelligence.

Having pioneered research in the world's leading academic and industrial labs, PhDs, post-docs, or professorships, Research Scientists join Google DeepMind to work collaboratively within and across Research fields. They are expected to work with teams on large scale AI, and develop solutions to fundamental questions in machine learning and AI.

Drawing on expertise from a variety of disciplines including deep learning, computer vision, language modeling, and advanced generative architectures, our Research Scientists are at the forefront of groundbreaking research.

Responsibilities

• Design, rapidly implement, and rigorously evaluate cutting-edge deep learning algorithms and data curation for multimodal generative AI, with a particular emphasis on culturally-adapted image and video synthesis.
• Report and present research findings and developments clearly and efficiently both internally and externally, verbally and in writing.
• Suggest and engage in team collaborations to meet ambitious research goals, while also driving significant individual contributions.
• Work in collaboration with our Ethics and Governance teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

Minimum Qualifications

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or equivalent practical experience.
• 2+ years of relevant experience in deep learning research and development, particularly in generative AI and related to image and video synthesis. This includes diffusion models and autoregressive generative models.
• Experience in software development with one or more programming languages (e.g., Python) and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch), with a track record of building high-quality research prototypes and systems.

Preferred Qualifications

• Demonstrated experience in large-scale training of multimodal generative models.
• A track record of research or engineering achievements, including publications in peer-reviewed conferences or journals.

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Key responsibilities include pioneering novel computer vision models to create a unified understanding of agriculture from diverse satellite data sources, solving core AI problems by developing generalizable models that are robust across varied agricultural systems, leading research toward the grand challenge of field-level crop yield forecasting, designing and executing large-scale experiments, writing high-quality, reusable code, and contributing to a production-ready system.

You will also mentor junior researchers, collaborate with cross-functional teams across Google, and publish your work at top-tier conferences.

We are looking for a passionate and talented researcher with a strong foundation and a proven ability to conduct impactful research in AI. You should have a PhD or equivalent practical research experience in Computer Science, AI, or a related field with a focus on computer vision and/or machine learning, a strong publication record in top-tier AI conferences, hands-on experience building and training deep learning models in frameworks such as JAX, TensorFlow, or PyTorch, and demonstrated expertise in one or more of the following: generative models, segmentation algorithms, multi-modal fusion, spatio-temporal analysis.

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Research includes, but is not limited to, general-purpose algorithms that leverage Large Language Model (LLMs) for efficient search and exploration, LLM fine-tuning with Reinforcement Learning, and open-ended tasks for large-scale AI-powered empirical research.

Responsibilities:

• Design, implement, and evaluate models, agents, and software prototypes of large foundational models.
• Push the boundary of state-of-the-art RL methods and machine learning optimization methods to build autonomous scientific discovery systems.
• Report and present research findings and developments, including status and results, clearly and efficiently both internally and externally, verbally and in writing.
• Suggest and engage in team collaborations to meet ambitious research goals.
• Work with external collaborators and maintain relationships with relevant research labs and key individuals as appropriate.
• Work in collaboration with our Responsible AI teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

About you:

• PhD in Computer Science, Artificial Intelligence, or a related field.
• Strong publication record in top-tier machine learning conferences or journals.
• Solid understanding of deep learning, natural language processing, and/or reinforcement learning.
• Experience with Large Language Models, preferably in the context of code synthesis.
• Experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch.
• A real passion for AI!

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The role of the Research Engineer will be to develop state-of-the-art methods for multimodal generative AI models, with a primary focus on image generation and editing. This role is for the team behind “Nano Banana”.

As a Research Engineer at Google DeepMind, you will lead our efforts in developing novel tools, infrastructure, and algorithms towards the end goal of solving and building Artificial General Intelligence. You will work collaboratively within and across Research fields, drawing on expertise from a variety of disciplines including deep learning, computer vision, language modeling, and advanced generative architectures.

Key responsibilities include designing, implementing, and evaluating cutting-edge deep learning algorithms, data curation, and evaluation infrastructure for multimodal generative AI, with a particular emphasis on image synthesis. You will report and present research findings and developments clearly and efficiently both internally and externally, verbally and in writing. You will also suggest and engage in team collaborations to meet ambitious research goals, while also driving significant individual contributions.

To succeed as a Research Engineer at Google DeepMind, we look for the following skills and experience:

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or equivalent practical experience.
• Proven experience in deep learning research and development, particularly in generative AI and related to image synthesis. This includes diffusion models and autoregressive generative models. Experience with post-training is a plus.
• Exceptional engineering skills in Python and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch), with a track record of building high-quality research prototypes and systems.
• Strong publication record at top-tier machine learning, computer vision, and graphics conferences (e.g., NeurIPS, ICLR, ICML, SIGGRAPH, CVPR, ICCV).

In addition, the following would be an advantage:

• Demonstrated experience in multimodal generative modeling, especially combining large language models with visual generation (e.g., text-to-image/video systems, joint autoregressive and diffusion models).
• A keen eye for visual aesthetics and detail, coupled with a passion for creating high-quality, visually compelling generative content.
• A real passion for AI!

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Your work may involve brainstorming new disruptive ideas that could become the next generation of frontier AI models, particularly within the text diffusion space. You will prototype and develop these ideas with the rest of the team, contributing directly to Gemini Diffusion research. You will solve key research challenges by designing and executing experimental research on text diffusion models, sharing analyses, and proposing next steps. You will rigorously validate the theoretical and practical impact of our work at a large scale. You will work collaboratively with other Generative AI teams to move the technologies we develop out of the lab and into production. You will advance the fundamental architecture, algorithmic design, and capabilities of large-scale diffusion models. You will bring deep scientific expertise into our projects, sharing your insights and knowledge with other researchers and engineers.

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As a Research Scientist at Google DeepMind, you will lead our efforts in developing novel algorithmic architecture towards the end goal of solving and building Artificial General Intelligence.

Key responsibilities include:

• Design, implement and evaluate models, agents and software prototypes of large foundational models.
• Push the boundary of state of the art RL methods and machine learning optimization methods to build autonomous agents.
• Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.
• Suggest and engage in team collaborations to meet ambitious research goals.
• Work with external collaborators and maintain relationships with relevant research labs and key individuals as appropriate.
• Work in collaboration with our Responsible AI teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

You are a passionate and talented researcher with a strong foundation and a proven ability to conduct impactful research in AI. You embrace change and thrive under ambiguity. You have a collaborative mindset and are excited to work as part of a team to tackle ambitious research challenges.

Requirements include:

• PhD in Computer Science, Artificial Intelligence, or a related field.
• Strong publication record in top-tier machine learning conferences or journals.
• Solid understanding of deep learning, natural language processing, computer vision, and/or speech processing.
• Experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch.

Preferred qualifications include experience with multimodal learning, large language models, and/or assistive AI agents.

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We are seeking a Research Engineer to join our Machine Learning team. As a Research Engineer, you will work on building and optimizing large-scale learning systems that power our open-weight models.

Responsibilities

• Accelerate researchers by taking on the heavy parts of large-scale ML pipelines and building robust tools.
• Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs.
• Conduct experiments on the latest deep-learning techniques.
• Design, implement and benchmark ML algorithms; write clear, efficient code in Python.
• Deliver prototypes that become production-grade components for Le Chat and our enterprise API.

Requirements

• Master's or PhD in Computer Science (or equivalent proven track record).
• 4 + years working on large-scale ML codebases.
• Hands-on with PyTorch, JAX or TensorFlow; comfortable with distributed training (DeepSpeed / FSDP / SLURM / K8s).
• Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops.
• Strong software-design instincts: testing, code review, CI/CD.
• Self-starter, low-ego, collaborative.

Benefits

• Competitive cash salary and equity
• Food: Daily lunch vouchers
• Sport: Monthly contribution to a Gympass subscription
• Transportation: Monthly contribution to a mobility pass
• Health: Full health insurance for you and your family
• Parental: Generous parental leave policy
• Visa sponsorship

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Mistral AI is a pioneering company that develops and provides high-performance, open-source AI models, products, and solutions.

Role Summary

The Research Engineering team at Mistral AI spans Platform (shared infrastructure and clean code) and Embedded (inside research squads). Engineers can move along the research↔production spectrum as needs or interests evolve.

As a Research Engineer – ML track, you’ll build and optimize the large-scale learning systems that power our open-weight models. Working hand-in-hand with Research Scientists, you’ll either join:

• Platform RE Team: Enhance the shared training framework, data pipelines, and cluster tooling used by every team;
• Embedded RE Team: Sit inside a research squad (Alignment, Pre-training, Multimodal, …) and turn fresh ideas into repeatable, scalable code.

Responsibilities

• Accelerate researchers by taking on the heavy parts of large-scale ML pipelines and building robust tools.
• Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs.
• Conduct experiments on the latest deep-learning techniques (sparsified 70 B + runs, distributed training on thousands of GPUs).
• Design, implement, and benchmark ML algorithms; write clear, efficient code in Python.
• Deliver prototypes that become production-grade components for Le Chat and our enterprise API.

Requirements

• Master’s or PhD in Computer Science (or equivalent proven track record).
• 4 + years working on large-scale ML codebases.
• Hands-on with PyTorch, JAX, or TensorFlow; comfortable with distributed training (DeepSpeed / FSDP / SLURM / K8s).
• Experience in deep learning, NLP, or LLMs; bonus for CUDA or data-pipeline chops.
• Strong software-design instincts: testing, code review, CI/CD.
• Self-starter, low-ego, collaborative.

What We Offer

• Competitive salary and equity.
• Healthcare: Medical/Dental/Vision covered for you and your family.
• Pension: 401K (6% matching).
• PTO: 18 days.
• Transportation: Reimburse office parking charges, or $120/month for public transport.
• Sport: $120/month reimbursement for gym membership.
• Meal stipend: $400 monthly allowance for meals (solution might evolve as we grow bigger).
• Visa sponsorship.
• Coaching: we offer BetterUp coaching on a voluntary basis.

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About the Open Source Software team Our OSS team is embedded in our Science team and works very closely with various engineering and marketing teams. All OSS team members can fluidly move on the production / research spectrum depending on where the needs are or where their interests lie

Responsibilities • Releasing our models to open-source platforms and libraries, e.g., vLLM, GitHub, Hugging Face • Maintaining Mistral’s open-source libraries (mistral-common, mistral-finetune, mistral-inference) • Create and maintain tooling and services: both internal facing (internal research) and external facing (open-source libraries) • Implement and optimize open-source and internal libraries for performance and accuracy, ensuring production readiness and employing cutting-edge technology and innovative approaches • Collaborate with the open-source community (PyTorch, vLLM, Hugging Face)

About you • Master’s degree in Computer Science, Machine Learning, Data Science, or a related field • Experience contributing to popular open-source libraries such as PyTorch, Tensorflow, JAX, vLLM, Transformers, Llama.cpp, ... • Passion for contributing to the open-source software ecosystem • Expert programming skills in Python, PyTorch, MLOps • Adaptable, proactive, and autonomous • Attention to detail and a drive to go the last mile to build almost perfect tools • Deep understanding of machine learning approaches, especially LLMs and algorithms • Low-ego, collaborative and have a real team player mindset

Now, it would be ideal if you have: • Experience with training and fine-tuning large language models (e.g., distillation, supervised fine-tuning, policy optimization) • Experience working with Slurm • Worked with research teams before • Experience as a core-maintainer of a popular ML open-source library

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As an Open-Source ML engineer in Computer Vision, you will work mainly with existing open-source libraries, such as Transformers and Datasets to boost the support for vision or multi-modal models and datasets. You will bring your computer vision expertise to provide the best computer-vision tool stack in the machine learning ecosystem and work with us to provide the best, simplest, and most intuitive computer-vision library in the industry.

Responsibilities:

• Work with existing open-source libraries to boost support for vision or multi-modal models and datasets.
• Bring computer vision expertise to provide the best computer-vision tool stack in the machine learning ecosystem.
• Collaborate with researchers, ML practitioners, and data scientists on a daily basis.
• Foster one of the most active machine learning communities, helping users contribute to and use the tools that you build.

Requirements:

• Deep expertise in computer vision: object detection, segmentation, generative models, or multimodal systems.
• Strong open-source presence: You’ve contributed significantly to CV libraries (e.g., OpenCV, Detectron2, MMDetection, or Hugging Face’s own transformers/diffusers), as a Core-Contributor or maintainer.
• Scalability mindset: Experience optimizing models for production, deploying at scale, or improving inference efficiency.
• Collaboration & mentorship: You enjoy working with cross-functional teams, reviewing PRs, and guiding junior contributors.
• Alignment with our mission: You believe in democratizing AI and want to empower millions of builders with state-of-the-art tools.

If you love open-source, are passionate about the new development of Transformers models in computer vision, have experience building, optimizing, and training such models in PyTorch and/or TensorFlow, serving them in production, and want to contribute to one of the fastest-growing ML libraries, then we can't wait to see your application!

If you're interested in joining us, but don't tick every box above, we still encourage you to apply! We're building a diverse team whose skills, experiences, and backgrounds complement one another. We're happy to consider where you might be able to make the biggest impact.

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We are building the fastest growing platform for AI builders with over 11 million users who collectively shared over 2M models, 700k datasets & 600k apps.

As an Open-Source ML engineer in Computer Vision, you will work mainly with existing open-source libraries, such as Transformers and Datasets to boost the support for vision or multi-modal models and datasets.

You will bring your computer vision expertise to provide the best computer-vision tool stack in the machine learning ecosystem and work with us to provide the best, simplest, and most intuitive computer-vision library in the industry.

You'll get to foster one of the most active machine learning communities, helping users contribute to and use the tools that you build.

You'll interact with Researchers, ML practitioners, and data scientists on a daily basis through GitHub, our forums, or slack.

Responsibilities

• Deep expertise in computer vision: object detection, segmentation, generative models, or multimodal systems.
• Strong open-source presence: You’ve contributed significantly to CV libraries (e.g., OpenCV, Detectron2, MMDetection, or Hugging Face’s own transformers/diffusers), as a Core-Contributor or maintainer.
• Scalability mindset: Experience optimizing models for production, deploying at scale, or improving inference efficiency.
• Collaboration & mentorship: You enjoy working with cross-functional teams, reviewing PRs, and guiding junior contributors.
• Alignment with our mission: You believe in democratizing AI and want to empower millions of builders with state-of-the-art tools.

Benefits

• Flexible working hours and remote options.
• Health, dental, and vision benefits for employees and their dependents.
• Parental leave and flexible paid time off.
• Reimbursement for relevant conferences, training, and education.
• Company equity as part of their compensation package.

What We Offer

• Work with some of the smartest people in our industry.
• A bias for impact and a continuous growth mindset.
• Support for your well-being and career development.
• Opportunities to visit our offices in NYC and Paris.
• An outfitting of your workstation to ensure success.

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At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life.

About The Job

Mistral AI is seeking an Applied AI Engineer focused on DevOps to facilitate the adoption of its products among customers and collaborate with them to address complex technical challenges. Applied AI Engineers, ML Infra at Mistral AI work directly with customers to quickly understand their greatest challenges and design and implement AI solutions.

Responsibilities

• Onboard customers on our products, providing guidance on deployment and integration, and ensuring the best production setup from the low-level GPU stack up to infrastructure, back-end and front-end interfaces.
• Work on deploying state-of-the-art AI applications from consumer products to industrial use cases, driving with our customers a crucial technological transformation.
• Collaborate with our researchers, other AI engineers, and product engineers on our most complex customer projects involving deployment, scaling, and contributing to our open-source codebases for tasks such as inference and fine-tuning.
• Involved in pre-sales calls to understand potential clients' needs, challenges, and aspirations. Provide technical guidance on our products and explain Mistral technologies to various stakeholders.

About You

• Fluent in English
• Hold a Bachelor's or Master's degree in Computer Science, Engineering, or a related field
• 5+ years of experience in a DevOps or Site Reliability Engineering role
• Experienced with deploying and managing AI-based products in production environments
• Fluent in Python
• Experience with containerization technologies such as Docker and Kubernetes
• Experience with CI/CD pipelines and automated deployment tools
• Deep understanding of cloud platforms (AWS, Azure, GCP) and on-premises infrastructure
• Experienced with infrastructure as code (IaC) tools such as Terraform or Ansible
• Strong communication skills with an ability to explain complex technical concepts in simple terms to technical and non-technical audiences

Ideally You Have

• Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect, or Technical Product Manager
• Familiarity with AI frameworks such as PyTorch or TensorFlow
• Contributions to open-source projects, particularly in the space of DevOps or AI

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As part of the Solutions Architecture team, we work with some of the most exciting computing hardware and software technologies including the latest breakthroughs in machine learning and data science. A Solutions Architect is the first line of technical expertise between NVIDIA and our customers so you will engage directly with developers, researchers, and data scientists with some of NVIDIA's most strategic technology customers as well as work directly with business and engineering teams on product strategy.

What you will be doing:

• Working with Cloud Service Providers to develop and demonstrate solutions based on NVIDIA's ML/DL and data science software and hardware technologies

• Build and deploy AI/ML solutions at scale using NVIDIA's AI software on cloud-based GPU platforms.

• Build custom PoCs for solution that address customer's critical business needs applying NVIDIA hardware and software technology

• Partner with Sales Account Managers or Developer Relations Managers to identify and secure new business opportunities for NVIDIA products and solutions for ML/DL and other software solutions

• Prepare and deliver technical content to customers including presentations about purpose-built solutions, workshops about NVIDIA products and solutions, etc.

• Conduct regular technical customer meetings for project/product roadmap, feature discussions, and intro to new technologies. Establish close technical ties to the customer to facilitate rapid resolution of customer issues

What we need to see:

• 3+ years of Solutions Engineering (or similar Sales Engineering roles) or equivalent experience

• 3+ years of work-related experience in Deep Learning and Machine Learning, including deep learning frameworks TensorFlow or PyTorch, GPU, and CUDA experience extremely helpful.

• BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Statistics, Physics, or other Engineering fields or equivalent experience.

• Established track record of deploying solutions in cloud computing environments including AWS, GCP, or Azure

• Knowledge of DevOps/ML Ops technologies such as Docker/containers, Kubernetes, data center deployments

• Ability to use at least one scripting language (i.e., Python)

• Good programming and debugging skills

• Ability to communicate your ideas/code clearly through documents, presentation etc.

Ways to stand out from the crowd:

• AWS, GCP or Azure Professional Solution Architect Certification.

• Hands-on experience with NVIDIA GPUs and SDKs (e.g. CUDA, RAPIDS, Triton etc.)

• System-level experience specifically GPU-based systems

• Experience with Deep Learning at scale

• Familiarity with parallel programming and distributed computing platforms

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We are looking for a Solution Architect with deep expertise in AI solutions to drive the efficient use of groundbreaking compute platforms across industries. As a trusted technical advisor to our CAE developers and customers, you will be responsible for embedding NVIDIA software into developers' architectures and workflows.

What you'll be doing:

• Support Business Development and Sales teams as part of a team of 4, partnering with Industry Business leads, Account Managers, and Developer Relations managers to drive our developers' ecosystem success.
• Work directly with developers and customers in a customer-facing setting.
• Support developers in adopting NVIDIA libraries and software frameworks as the foundation for modern AI and data platforms.
• Analyze application architectures and find opportunities for acceleration.
• Provide feedback and collaborate with engineering, product, and research teams.
• Deliver trainings, hackathons, and technical demonstrations on NVIDIA solutions and platforms.

What we need to see:

• A MS/PhD degree in Machine Learning, Computational Science, Physics, or a related technical field.
• Minimum of 5 years of technical experience in Physics-Machine Learning.
• Experience in engineering simulations (e.g. fluid dynamics, atmospheric science, Computer-Aided Engineering technologies).
• Familiarity with accelerated computing platforms and GPU-based distributed systems.
• Experience in algorithm programming using languages like Python and C/C++.
• Development experience using major AI frameworks (e.g., PyTorch, Tensorflow, and similar tools).
• Familiarity with containers, numerical libraries, modular software design, version control, GitHub.
• Experience designing, prototyping, and building complex AI/ML-based solutions for customers.
• Able to reason across components such as data pipelines, models, compute, networking, and orchestration.
• Solid written and oral communications skills and familiarity with collaborative environments.
• Great teammate who can learn, react, and adapt quickly with a mentality to work for a fast-paced environment.

Ways to stand out from the crowd:

• Development experience with NVIDIA software libraries and GPUs.
• Experience with Kubernetes, distributed training, and large-scale inference.
• Experience supporting or utilizing PCIe accelerators such as GPUs, FPGAs, DSPs from evaluation to production stages.

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Fuse Energy is a forward-thinking renewable energy startup on a mission to deliver a terawatt of renewable energy - fast. We're combining first-principles thinking with cutting-edge technology to build a radically better energy system.

We're creating a fully integrated energy company: from developing solar, wind and hydrogen projects to real-time power trading and distributed energy installations. By selling directly to consumers, we cut out the middleman, lower costs and pass on savings to customers.

But we’re not stopping there. We’re also building the Energy Network: a decentralised platform of smart devices that rewards users in Energy Dollars for electrifying their homes, shifting usage to off-peak hours, and helping balance the grid. This network strengthens grid stability - a critical foundation for scaling AI data centers and other energy-intensive industries.

Responsibilities

• Design, develop and deploy AI-powered features that directly impact consumer experiences, including personalised energy recommendations and seamless onboarding via AI models.
• Build and optimise internal AI tools that will make the whole company more productive with a focus on automation and enhancing workflows.
• Collaborate with backend engineers and data scientists to integrate AI-driven features into our platforms.
• Collaborate with the trading and operations teams to ensure AI models are aligned with real-time market conditions and energy pricing.
• Improve AI models to optimise trading strategies by anticipating market shifts based on weather and demand forecasts.
• Stay up to date with the latest advancements in applied AI and machine learning and apply them to solve real-world problems within the energy space.
• Monitor the performance of AI tools and models, ensuring they are functioning efficiently and effectively.

Requirements

• Minimum 3 years of engineering experience
• Proven experience as a Backend Engineer with a strong interest and practical experience in applied AI or machine learning
• Strong programming skills in Python (or similar languages) with familiarity in AI/ML libraries (TensorFlow, PyTorch, etc.)
• Experience working with large-scale models (LLMs/VLMs) and deploying AI-driven solutions into production
• Solid understanding of cloud technologies, containerisation and building scalable AI applications
• Ability to integrate AI/ML models into real-world applications, focusing on usability and performance
• Strong problem-solving skills and a practical approach to implementing AI solutions in a fast-paced environment
• Experience working with large datasets, particularly in relation to demand and supply forecasting

Benefits

• Competitive salary and an equity sign-on bonus
• Biannual bonus scheme
• Fully expensed tech to match your needs
• Paid annual leave
• Breakfast and dinner allowance for office based employees

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We are seeking an experienced AI/GenAI engineer to join our team at Synopsys. As a Principal AI/GenAI Engineer, you will design, develop, and deploy GenAI Agentic and machine learning models, algorithms, and systems to tackle complex business challenges.

What You'll Be Doing:

• Design, develop, and deploy GenAI Agentic and machine learning models, algorithms, and systems to tackle complex business challenges.
• Provide technical leadership and mentorship, supporting junior engineers and data scientists in best practices and advanced techniques.
• Stay abreast of the latest AI, Agentic AI, and machine learning advancements, applying them to enhance existing systems or develop innovative solutions.
• Collaborate with program and product managers, software engineers, and stakeholders to define requirements and create technical specifications for AI solutions.
• Conduct data analysis, preprocessing, and feature engineering to prepare datasets for machine learning models.
• Train, validate, and fine-tune machine learning models to meet performance and accuracy benchmarks.
• Deploy AI models into production environments, monitoring and optimizing their performance.
• Document models, algorithms, and methodologies to ensure reproducibility and facilitate knowledge sharing.
• Ensure all AI solutions comply with ethical guidelines, data privacy regulations, and industry standards.

The Impact You Will Have:

• Accelerate innovation in AI-driven products and solutions, directly influencing Synopsys' technological leadership.
• Enhance operational efficiency and decision-making through intelligent automation and data-driven insights.
• Drive adoption of Agentic AI systems, enabling transformative business outcomes across multiple domains.
• Mentor and elevate the technical capabilities of the engineering and data science teams, fostering a culture of excellence.
• Champion ethical AI practices, ensuring responsible development and deployment of cutting-edge technologies.
• Support cross-functional collaboration, bridging the gap between technical and business teams for successful project delivery.
• Contribute to the advancement of semiconductor IPs and EDA tool integration with AI, expanding Synopsys' product portfolio.

What You'll Need:

• Ph.D. or Master’s degree in Computer Science, Data Science, Electrical Engineering, or a related field.
• Minimum 8 years of experience in software engineering, machine learning, and GenAI, with a proven track record in production deployments.
• Strong proficiency in Python; familiarity with backend and distributed systems, message queues, and orchestration technologies is a plus.
• Expertise in data structures, algorithms, and design patterns for scalable AI solutions.
• Extensive experience with machine learning and Agentic frameworks (e.g., TensorFlow, PyTorch, LangChain/LangGraph, AutoGen) and LLM models.
• Strong grasp of statistical analysis, data mining, and data visualization techniques.
• Knowledge of cloud platforms, containerization (Kubernetes, Docker), and version control systems (Git, P4).
• Familiarity with Agile, Kanban, or Scrum methodologies.
• Understanding or experience with semiconductor IPs and EDA tools is highly desirable.

Who You Are:

• Exceptional problem-solving and analytical abilities, with a strategic mindset.
• Strong communication and interpersonal skills to engage diverse audiences.
• Ability to work independently and collaboratively within multidisciplinary teams.
• Demonstrated leadership and mentorship capabilities.
• Adaptable, proactive, and eager to continuously learn and innovate.

The Team You’ll Be A Part Of:

You will join a dynamic, collaborative group of professionals dedicated to advancing AI and GenAI solutions for diverse projects. The team values innovation, knowledge sharing, and continuous improvement, working closely with product managers, engineers, and stakeholders to deliver impactful AI-powered systems. You will play a key role in mentoring, technical leadership, and driving the evolution of Synopsys’ AI initiatives.

Rewards and Benefits:

We offer a comprehensive range of health, wellness, and financial benefits to cater to your needs. Our total rewards include both monetary and non-monetary offerings. Your recruiter will provide more details about the salary range and benefits during the hiring process.

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The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the team

The Safety Systems org ensures that OpenAI’s most capable models can be responsibly developed and deployed. We build evaluations, safeguards, and safety frameworks that help our models behave as intended in real-world settings.

The Preparedness team is an important part of the [Safety Systems](https://openai.com/safety/safety-systems) org at OpenAI, and is guided by OpenAI’s [Preparedness Framework](https://openai.com/index/updating-our-preparedness-framework/).

Frontier AI models have the potential to benefit all of humanity, but also pose increasingly severe risks. To ensure that AI promotes positive change, the Preparedness team helps us prepare for the development of increasingly capable frontier AI models. This team is tasked with identifying, tracking, and preparing for catastrophic risks related to frontier AI models.

The mission of the Preparedness team is to:

1. Closely monitor and predict the evolving capabilities of frontier AI systems, with an eye towards risks whose impact could be catastrophic

1. Ensure we have concrete procedures, infrastructure and partnerships to mitigate these risks and to safely handle the development of powerful AI systems

About the role

Models are becoming increasingly capable—moving from tools that assist humans to agents that can plan, execute, and adapt in the real world. As we push toward AGI, cybersecurity becomes one of the most important and urgent frontiers: the same systems that can accelerate productivity can also accelerate exploitation.

As a Researcher for cybersecurity risks, you will help design and implement an end-to-end mitigation stack to reduce severe cyber misuse across OpenAI’s products. This role requires strong technical depth and close cross-functional collaboration to ensure safeguards are enforceable, scalable, and effective. You’ll contribute directly to building protections that remain robust as products, model capabilities, and attacker behaviors evolve.

In this role, you will:

• Design and implement mitigation components for model-enabled cybersecurity misuse—spanning prevention, monitoring, detection, and enforcement—under the guidance of senior technical and risk leadership.

• Integrate safeguards across product surfaces in partnership with product and engineering teams, helping ensure protections are consistent, low-latency, and scale with usage and new model capabilities.

• Evaluate technical trade-offs within the cybersecurity risk domain (coverage, latency, model utility, and user privacy) and propose pragmatic, testable solutions.

• Collaborate closely with risk and threat modeling partners to align mitigation design with anticipated attacker behaviors and high-impact misuse scenarios.

• Execute rigorous testing and red-teaming workflows, helping stress-test the mitigation stack against evolving threats (e.g., novel exploits, tool-use chains, automated attack workflows) and across different product surfaces—then iterate based on findings.

You might thrive in this role if you:

• Have a passion for AI safety and are motivated to make cutting-edge AI models safer for real-world use.

• Bring demonstrated experience in deep learning and transformer models.

• Are proficient with frameworks such as PyTorch or TensorFlow.

• Possess a strong foundation in data structures, algorithms, and software engineering principles.

• Are familiar with methods for training and fine-tuning large language models, including distillation, supervised fine-tuning, and policy optimization.

• Excel at working collaboratively with cross-functional teams across research, security, policy, product, and engineering.

• Have significant experience designing and deploying technical safeguards for abuse prevention, detection, and enforcement at scale.

• (Nice to have) Bring background knowledge in cybersecurity or adjacent fields.

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Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

Responsibilities Visual Intelligence Development: Build and deploy SOTA machine learning models for image classification, object detection, and video action recognition to power Bing’s multimedia features.

Multimodal & Generative AI: Lead the development of multimodal embeddings that align text and visual data, and leverage Generative AI (e.g., DALL-E, MAI models) to enhance content creation tools.

Scale & Optimization: Design robust feature-engineering pipelines to process billions of images and videos, ensuring low-latency inference in production services.

Strategic Leadership: Embody Microsoft’s values by Creating Clarity in complex AI problems and Generating Energy across cross-functional teams of engineers and PMs.

Responsible AI: Ensure all visual models adhere to strict Security, Privacy, and GDPR standards, specifically focusing on content moderation and bias detection in multimedia.

Qualifications Required Qualifications: Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics predictive analytics, research) OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Mastery of Python and deep learning frameworks such as PyTorch or TensorFlow. Proven track record in Computer Vision (CV) or Multimedia Understanding, including work with large-scale visual datasets. Experience building and deploying live production systems at scale.

Preferred Qualifications: PhD focused on Computer Vision, Video Analytics, or Multimodal Learning. Experience with big data tools like Spark/PySpark and Azure Machine Learning. Publications in top-tier venues such as CVPR, ICCV, or ACM Multimedia.

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role

As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.

Representative projects:

• Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows.
• Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.
• Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows.
• Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.

You may be a good fit if you:

• Are proficient in Python and async/concurrent programming with frameworks like Trio
• Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)
• Have industry experience in machine learning research
• Can balance research exploration with engineering implementation
• Enjoy pair programming (we love to pair!)
• Care about code quality, testing, and performance
• Have strong systems design and communication skills
• Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems

Strong candidates may have:

• Familiarity with LLM architectures and training methodologies
• Experience with reinforcement learning techniques and environments
• Experience with virtualization and sandboxed code execution environments
• Experience with Kubernetes
• Experience with distributed systems or high-performance computing
• Experience with Rust and/or C++

Strong candidates need not have:

• Formal certifications or education credentials
• Academic research experience or publication history

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Your safety matters to us. To protect yourself from potential

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We are seeking a Machine Learning Infrastructure Engineer to join our Safeguards organization, where you'll build and scale the critical infrastructure that powers our AI safety systems. You'll work at the intersection of machine learning, large-scale distributed systems, and AI safety, developing the platforms and tools that enable our safeguards to operate reliably at scale.

Responsibilities:

• Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem
• Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications
• Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems
• Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards
• Implement automated testing, deployment, and rollback systems for ML models in production safety applications
• Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs
• Contribute to the development of internal tools and frameworks that accelerate safety research and deployment

You may be a good fit if you:

• Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment
• Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX
• Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)
• Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads
• Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems)
• Are results-oriented, with a bias towards reliability and impact in safety-critical systems
• Enjoy collaborating with researchers and translating cutting-edge research into production systems
• Care deeply about AI safety and the societal impacts of your work

Strong candidates may have experience with:

• Working with large language models and modern transformer architectures
• Implementing A/B testing frameworks and experimentation infrastructure for ML systems
• Developing monitoring and alerting systems for ML model performance and data drift
• Building automated labeling systems and human-in-the-loop workflows
• Experience in trust & safety, fraud prevention, or content moderation domains
• Knowledge of privacy-preserving ML techniques and compliance requirements
• Contributing to open-source ML infrastructure projects

Deadline to apply:

None. Applications will be reviewed on a rolling basis.

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification.

Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us.

To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing the state of the art in AI safety and making a meaningful difference in the world.

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As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.

Representative projects:

• Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows.

• Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.

• Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows.

• Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.

You may be a good fit if you:

• Are proficient in Python and async/concurrent programming with frameworks like Trio

• Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)

• Have industry experience in machine learning research

• Can balance research exploration with engineering implementation

• Enjoy pair programming (we love to pair!)

• Care about code quality, testing, and performance

• Have strong systems design and communication skills

• Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems

Strong candidates may have:

• Familiarity with LLM architectures and training methodologies

• Experience with reinforcement learning techniques and environments

• Experience with virtualization and sandboxed code execution environments

• Experience with Kubernetes

• Experience with distributed systems or high-performance computing

• Experience with Rust and/or C++

Strong candidates need not have:

• Formal certifications or education credentials

• Academic research experience or publication history

Deadline to apply: None. Applications will be reviewed on a rolling basis.

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:

£260,000 - £630,000GBP

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic is a legitimate company and we will never ask you to pay any fees or provide sensitive information via email or phone.

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As an Enterprise Product Engineer at Cursor, you'll architect, implement, and deploy projects end-to-end to build enterprise-grade features that help large organisations adopt and scale with Cursor.

You may be a fit if

You have an entrepreneurial spirit and love creating outsized business impact. You want to be at the frontier of AI transformation with the best companies in the world. You're passionate about building great products that blend excellent engineering with a taste for models and design. You have a propensity for creative ideas and have a knack for making powerful tools without compromising their ease-of-use.

Responsibilities

• Architect, implement, and deploy projects end-to-end to build enterprise-grade features that help large organisations adopt and scale with Cursor.
• Collaborate with cross-functional teams to define and deliver product roadmaps that meet business objectives.
• Analyse customer needs and develop solutions that meet their requirements.
• Work closely with the design team to create user-centred products that are both functional and aesthetically pleasing.
• Develop and maintain high-quality code that is scalable, maintainable, and efficient.
• Participate in code reviews to ensure that the codebase is of the highest quality.
• Stay up-to-date with the latest technologies and trends in the industry.

Benefits

• Competitive salary and benefits package.
• Opportunity to work with a recognised leader in the AI industry.
• Collaborative and dynamic work environment.
• Flexible working hours and remote work options.
• Access to the latest technologies and tools.
• Opportunities for professional growth and development.

What we're looking for

• 3+ years of experience in software development, preferably in a product engineering role.
• Strong understanding of software development principles, patterns, and best practices.
• Experience with Agile development methodologies and version control systems.
• Strong problem-solving skills and attention to detail.
• Excellent communication and collaboration skills.
• Experience with cloud-based technologies and containerisation.
• Familiarity with machine learning and AI concepts.
• Experience with design thinking and user-centred design.
• Strong understanding of security principles and best practices.
• Experience with DevOps practices and tools.
• Familiarity with testing frameworks and methodologies.
• Experience with continuous integration and continuous deployment.
• Strong understanding of scalability and performance optimisation.
• Experience with monitoring and logging tools.
• Familiarity with containerisation and orchestration.
• Experience with cloud-based storage and databases.
• Familiarity with security frameworks and best practices.
• Experience with compliance and regulatory requirements.
• Familiarity with industry standards and best practices.

Preferred skills

• Experience with Python, Java, or C++.
• Familiarity with cloud-based platforms such as AWS or Azure.
• Experience with containerisation and orchestration tools such as Docker and Kubernetes.
• Familiarity with machine learning and AI frameworks such as TensorFlow or PyTorch.
• Experience with design thinking and user-centred design tools such as Sketch or Figma.
• Familiarity with testing frameworks and methodologies such as JUnit or PyUnit.
• Experience with continuous integration and continuous deployment tools such as Jenkins or GitLab CI/CD.
• Familiarity with monitoring and logging tools such as Prometheus or Grafana.
• Experience with security frameworks and best practices such as OWASP or NIST.
• Familiarity with compliance and regulatory requirements such as GDPR or HIPAA.
• Experience with industry standards and best practices such as ISO 27001 or PCI-DSS.

Salary range

£80,000 - £120,000 per annum.

Category

Engineering.

Industry

Technology.

Experience level

Mid.

Employment type

Full-time.

Workplace type

Remote.

Required skills

• Software development principles, patterns, and best practices.
• Agile development methodologies and version control systems.
• Problem-solving skills and attention to detail.
• Communication and collaboration skills.
• Cloud-based technologies and containerisation.
• Machine learning and AI concepts.
• Design thinking and user-centred design.
• Security principles and best practices.
• DevOps practices and tools.
• Testing frameworks and methodologies.
• Continuous integration and continuous deployment.
• Scalability and performance optimisation.
• Monitoring and logging tools.
• Containerisation and orchestration.
• Cloud-based storage and databases.
• Security frameworks and best practices.
• Compliance and regulatory requirements.
• Industry standards and best practices.

Preferred skills

• Python, Java, or C++.
• Cloud-based platforms such as AWS or Azure.
• Containerisation and orchestration tools such as Docker and Kubernetes.
• Machine learning and AI frameworks such as TensorFlow or PyTorch.
• Design thinking and user-centred design tools such as Sketch or Figma.
• Testing frameworks and methodologies such as JUnit or PyUnit.
• Continuous integration and continuous deployment tools such as Jenkins or GitLab CI/CD.
• Monitoring and logging tools such as Prometheus or Grafana.
• Security frameworks and best practices such as OWASP or NIST.
• Compliance and regulatory requirements such as GDPR or HIPAA.
• Industry standards and best practices such as ISO 27001 or PCI-DSS.

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Responsibilities

• Work closely with the data engineering team to design, develop and maintain data pipelines and architectures
• Collaborate with cross-functional teams to identify business opportunities and develop data-driven solutions
• Develop and maintain machine learning models to drive business growth and improve customer experience
• Analyse large datasets to identify trends and insights that can inform business decisions
• Communicate complex data insights to non-technical stakeholders

Requirements

• Bachelor's degree in Computer Science, Mathematics, Statistics or a related field
• 2+ years of experience in data science or a related field
• Strong programming skills in languages such as Python, R or SQL
• Experience with machine learning libraries such as scikit-learn or TensorFlow
• Strong analytical and problem-solving skills
• Excellent communication and interpersonal skills

Benefits

• Competitive salary and benefits package
• Opportunity to work with a leading Formula One team
• Collaborative and dynamic work environment
• Professional development and growth opportunities
• Access to cutting-edge technology and tools
• Flexible working hours and remote work options

If you are a motivated and talented Data Scientist looking for a new challenge, please submit your application. We look forward to hearing from you!

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Location

San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Strategic Finance

Compensation

• $230K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Strategic Finance team at OpenAI plays a critical role in shaping the company’s long-term trajectory. We partner closely with Product, Engineering, and Go-To-Market teams to inform high-stakes decisions through rigorous data science and economic modeling. As part of our expanding Data Science function, we’re building a best-in-class Forecasting capability to drive real-time, data-driven decision-making across user growth, revenue, compute infrastructure, and more.

We are developing scalable forecasting infrastructure to help us understand and anticipate business dynamics in an increasingly complex, usage-based world. Our models are foundational to planning, pricing, operational efficiency, and growth strategy - supporting key investment decisions and unlocking OpenAI’s full potential.

About the Role

We’re looking for a senior Machine Learning Data Scientist to lead our forecasting initiatives. You’ll be one of the founding members of the Forecasting pillar within Strategic Finance Data Science, responsible for building and scaling robust, interpretable, and production-ready forecasting systems. Your models will power critical business decisions by predicting core metrics such as DAU/WAU, revenue, LTV, compute consumption, and profitability.

This is a highly cross-functional role, requiring technical excellence, strong product intuition, and business acumen. You’ll collaborate with product managers, researchers, engineers, and finance leaders to operationalize forecasting insights, influence company-wide strategy, and build foundational forecasting capabilities at OpenAI.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Build statistical and machine learning models to solve forecasting needs across product, finance, infrastructure, and GTM domains.

• Own the end-to-end modeling lifecycle, including scoping, feature engineering, model development and prototyping, experimentation, deployment, monitoring, and explainability.

• Develop and productionize scalable, interpretable forecasts for user growth, monetization, compute load, customer lifetime value, and profitability.

• Contribute to self-service forecasting tools and internal platforms, enabling teams across OpenAI to access and act on real-time predictions.

• Research and evaluate emerging tools and techniques in the forecasting space, such as TimeGPT, large language model extensions, causal forecasting, and hybrid approaches.

• Drive strategic insight generation by translating technical outputs into business-aligned recommendations and decision frameworks.

• Collaborate closely with cross-functional teams to ensure forecasts are well-integrated into planning processes, experimentation workflows, and executive decision-making.

You might thrive in this role if you have:

• Advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Economics, Operations Research).

• 7+ years of experience in applied data science, with deep hands-on exposure to forecasting, predictive modeling, or marketplace systems.

• Expertise in time-series forecasting techniques and practical understanding of model trade-offs across performance, explainability, and scalability.

• Proficiency in Python, SQL, and tools such as scikit-learn, PyTorch/TensorFlow, and forecasting libraries.

• Demonstrated experience with model monitoring, debugging, and long-term maintenance in production environments.

• Strong communication and storytelling skills - able to simplify complexity and influence executive stakeholders.

• Self-directed, intellectually curious, and comfortable leading ambiguous projects from 0→1.

Bonus if

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San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Scaling

Compensation

• $250K – $445K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team Training Runtime designs the core distributed machine-learning training runtime that powers everything from early research experiments to frontier-scale model runs. With a dual mandate to accelerate researchers and enable frontier scale, we’re building a unified, modular runtime that meets researchers where they are and moves with them up the scaling curve.

Our work focuses on three pillars: high-performance, asynchronous, zero-copy tensor and optimizer-state-aware data movement; performant, high-uptime, fault-tolerant training frameworks (training loop, state management, resilient checkpointing, deterministic orchestration, and observability); and distributed process management for long-lived, job-specific and user-provided processes.

We integrate proven large-scale capabilities into a composable, developer-facing runtime so teams can iterate quickly and run reliably at any scale, partnering closely with model-stack, research, and platform teams. Success for us is measured by raising both training throughput (how fast models train) and researcher throughput (how fast ideas become experiments and products).

About the Role As a Training Performance Engineer, you’ll drive efficiency improvements across our distributed training stack. You’ll analyze large-scale training runs, identify utilization gaps, and design optimizations that push the boundaries of throughput and uptime. This role blends deep systems understanding with practical performance engineering — analyzing GPU kernel performance, collective communication throughput, investigating I/O bottlenecks, and sharding our models so we can train them at massive scale.

You’ll help ensure that our clusters are running at peak performance, enabling OpenAI to train larger, more capable models with the same compute budget.

This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Profile end-to-end training runs to identify performance bottlenecks across compute, communication, and storage.
• Optimize GPU utilization and throughput for large-scale distributed model training.
• Collaborate with runtime and systems engineers to improve kernel efficiency, scheduling, and collective communication performance.
• Implement model graph transforms to improve end to end throughput.
• Build tooling to monitor and visualize MFU, throughput, and uptime across clusters.
• Partner with researchers to ensure new model architectures scale efficiently during pre-training.
• Contribute to infrastructure decisions that improve reliability and efficiency of large training jobs.

You might thrive in this role if you:

• Love optimizing performance and digging into systems to understand how every layer interacts.
• Have strong programming skills in Python and C++ (Rust or CUDA a plus).
• Have experience running distributed training jobs on multi-GPU systems or HPC clusters.
• Enjoy debugging complex distributed systems and measuring efficiency rigorously.
• Have exposure to frameworks like PyTorch, JAX, or TensorFlow and an understanding of how large-scale training loops are built.
• Are comfortable collaborating across teams and translating raw profiling data into practical engineering improvements.

Nice to have:

• Familiarity with NCCL, MPI, or UCX communication libraries.
• Experience with large-scale data loading and checkpointing systems.
• Prior work on training runtime, distributed scheduling, or ML compiler optimization.

About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is

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Research Engineer / Machine Learning Engineer - B2B Applications

Location

San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Applied AI

Compensation

• $295K – $445K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

OpenAI is at the forefront of artificial intelligence, driving innovation and shaping the future with cutting-edge research. Our mission is to ensure that AI's benefits reach everyone. We are looking for visionary Research Engineers to join our Applied Voice Team, where you'll conduct groundbreaking research on speech models and transform it into real-world applications that can change industries, enhance human creativity, and solve complex problems.

About the Role

As a Research Engineer in OpenAI's Applied Voice Team, you will have the opportunity to work with some of the brightest minds in AI. You'll design and build state-of-the-art speech models (speech-to-speech, transcribing, text to speech, etc.) and help turn research breakthroughs into tangible solutions in B2B applications, API and ChatGPT AVM. If you're excited about making AI technology accessible and impactful, this role is your chance to make a significant mark.

In this role, you will:

• Innovate and Build: Design and build advanced machine learning models that solve real-world problems. Bring OpenAI's research from concept to implementation, creating AI-driven applications with a direct impact.

• Collaborate with the Best: Work closely with software engineers, product managers and forward deployed engineers to understand complex business challenges, address customer concerns and deliver AI-powered solutions. Be part of a dynamic team where ideas flow freely and creativity thrives.

• Optimize and Scale: Implement scalable data pipelines, optimize models for performance and accuracy, and ensure they are production-ready. Contribute to projects that require cutting-edge technology and innovative approaches.

• Learn and Lead: Stay ahead of the curve by engaging with the latest developments in machine learning and AI. Take part in code reviews, share knowledge, and lead by example to maintain high-quality engineering practices.

• Make a Difference: Monitor and maintain deployed models to ensure they continue delivering value. Your work will directly influence how AI benefits individuals, businesses, and society at large.

You might thrive in this role if you:

• Master's/ PhD degree in Computer Science, Machine Learning, or a related field.

• 2+ years of professional engineering experience (excluding internships) in relevant roles at tech and product-driven companies.

• Demonstrated experience in deep learning and transformers models

• Proficiency in frameworks like PyTorch or Tensorflow

• Strong foundation in data structures, algorithms, and software engineering principles.

• Are familiar with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization

• Experience with speech models is a plus.

• Excellent problem-solving and analytical skills, with a proactive approach to challenges.

• Ability to work collaboratively with cross-functional teams.

• Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines

• Enjoy owning the problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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Location

San Francisco

Employment Type

Full time

Department

Applied AI

Compensation

• $250K – $555K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

OpenAI is at the forefront of artificial intelligence, driving innovation and shaping the future with cutting-edge research. Our mission is to ensure that AI's benefits reach everyone. We are looking for visionary Research Engineers to join our Applied Group, where you'll transform groundbreaking research into real-world applications that can change industries, enhance human creativity, and solve complex problems.

About the Role

As a Research Engineer in OpenAI's Applied Group, you will have the opportunity to work with some of the brightest minds in AI. You'll contribute to deploying state-of-the-art models in production environments, helping turn research breakthroughs into tangible solutions. If you're excited about making AI technology accessible and impactful, this role is your chance to make a significant mark.

In this role, you will:

• Innovate and Deploy: Design and deploy advanced machine learning models that solve real-world problems. Bring OpenAI's research from concept to implementation, creating AI-driven applications with a direct impact.

• Collaborate with the Best: Work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Be part of a dynamic team where ideas flow freely and creativity thrives.

• Optimize and Scale: Implement scalable data pipelines, optimize models for performance and accuracy, and ensure they are production-ready. Contribute to projects that require cutting-edge technology and innovative approaches.

• Learn and Lead: Stay ahead of the curve by engaging with the latest developments in machine learning and AI. Take part in code reviews, share knowledge, and lead by example to maintain high-quality engineering practices.

• Make a Difference: Monitor and maintain deployed models to ensure they continue delivering value. Your work will directly influence how AI benefits individuals, businesses, and society at large.

You might thrive in this role if you:

• Master's/ PhD degree in Computer Science, Machine Learning, Data Science, or a related field.

• Demonstrated experience in deep learning and transformers models

• Proficiency in frameworks like PyTorch or Tensorflow

• Strong foundation in data structures, algorithms, and software engineering principles.

• Experience with search relevance, ads ranking or LLMs is a plus.

• Are familiar with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization

• Excellent problem-solving and analytical skills, with a proactive approach to challenges.

• Ability to work collaboratively with cross-functional teams.

• Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines

• Enjoy owning the problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

XML job scraping automation by YubHub

XML job scraping automation by YubHub