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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Your customer will be other MLREs and AAIs on the Enterprise AI team who are taking the training algorithms and applying them to client use-cases ranging from next-generation AI cybersecurity firewall LLMs to training foundation healthtech search models.

If you are excited about shaping the future of the modern AI movement, we would love to hear from you!

Key Responsibilities:

• Build, profile and optimize our training and inference framework.
• Post-train state of the art models, developed both internally and from the community, to define stable post-training recipes for our enterprise engagements.
• Collaborate with ML teams to accelerate their research and development, and enable them to develop the next generation of models and data curation.
• Create a next-gen agent training algorithm for multi-agent/multi-tool rollouts.

Ideal Candidate:

• At least 1-3 years of LLM training in a production environment.
• Passionate about system optimization.
• Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
• Ability to demonstrate know-how on how to operate the architecture of the modern GPU cluster.
• Experience with multi-node LLM training and inference.
• Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc.
• Strong written and verbal communication skills to operate in a cross functional team environment.
• PhD or Masters in Computer Science or a related field.

Compensation:

We offer competitive compensation packages, including 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.

Benefits:

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

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You will work closely with Scale’s ML teams and researchers to build the foundation platform which supports all our ML research and development works. You will be building and optimising the platform to enable our next generation LLM training, inference and data curation.

Key responsibilities include:

• Building, profiling and optimising our training and inference framework.
• Collaborating with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation.
• Researching and integrating state-of-the-art technologies to optimise our ML system.

The ideal candidate will have:

• Passionate about system optimisation.
• Experience with multi-node LLM training and inference.
• Experience with developing large-scale distributed ML systems.
• Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
• Strong software engineering skills, proficient in frameworks and tools such as CUDA, PyTorch, transformers, flash attention, etc.

Nice to haves include demonstrated expertise in post-training methods and/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.

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.

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Scale's ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM's, as well as evaluation of data quality.

At Scale, we're uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale's ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation.

Responsibilities:

• Build, profile and optimize our training and inference framework
• Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation
• Research and integrate state-of-the-art technologies to optimize our ML system

Ideal Candidate:

• Strong excitement about system optimization
• Experience with multi-node LLM training and inference
• Experience with developing large-scale distributed ML systems
• Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc.
• Strong written and verbal communication skills and the ability to operate in a cross functional team environment

Nice to Have:

• Demonstrated expertise in post-training methods &/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.

Compensation Packages:

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.

Please note that our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

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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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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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Strong candidates will have a track record of solving large-scale systems problems and low-level optimization. They should have significant experience optimizing ML systems for TPUs, GPUs, or other accelerators, and be results-oriented with a bias towards flexibility and impact.

Responsibilities:

• Identify and address performance issues across multiple ML systems
• Design and optimize kernels for the TPU
• Provide feedback to researchers on model changes and their impact on performance

Requirements:

• Bachelor's degree or equivalent combination of education, training, and/or experience
• Relevant field of study
• Years of experience required will correlate with the internal job level requirements for the position

Benefits:

• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Lovely office space in which to collaborate with colleagues

Note: This job description is a rewritten version of the original ad, focusing on the key responsibilities, requirements, and benefits.

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We're a multidisciplinary team building state-of-the-art generative models in chemistry & materials to accelerate scientific breakthroughs.

As a Research Scientist in our Science unit, you will be at the forefront of applying generative AI to the "Grand Challenge" of predicting the structure and properties of complex matter.

Your work will bridge the gap between in silico modeling and real-world laboratory discovery, particularly in areas where traditional computational methods are bottlenecked by time and complexity.

Key responsibilities:

• Design and train advanced AI models (transformers, generative models, etc.) to model the structure and properties of complex physical systems.

• Develop deep understanding of scientific domains that can be used to identify novel modelling approaches.

• Design and execute robust ML experiments that allow for the accumulation of small improvements, sharing results through clear verbal and written communication.

• Collaborate with other scientists and engineers to help shape the research roadmap.

About You

You are fascinated by the intersection of AI and natural science and determined to help solve grand challenges facing humanity.

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

• PhD / equivalent experience in computer science, computational chemistry, materials science, physics with a focus on atomistic simulation or structural biology.

• Fluency in generative models and transformers

• Excellent collaboration and communication skills

• Experience with modern deep learning frameworks

In addition, the following would be an advantage:

• Record of high-impact published work at the intersection of AI and natural science, particularly chemistry and materials science.

• Demonstrated experience in geometric deep learning.

Applications close on Monday 20th April at 5pm BST

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About the Role:

When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?" The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe.

Think of us as doing "neuroscience" of neural networks using "microscopes" we build - or reverse-engineering neural networks like binary programs.

More resources to learn about our work:

• Our research blog - covering advances including Monosemantic Features and Circuits

• An Introduction to Interpretability from our research lead, Chris Olah

• The Urgency of Interpretability from CEO Dario Amodei

• Engineering Challenges Scaling Interpretability - directly relevant to this role

• 60 Minutes segment - Around 8:07, see a demo of tooling our team built

• New Yorker article - what it's like to work on one of AI's hardest open problems

Even if you haven't worked on interpretability before, the infrastructure expertise is similar to what's needed across the lifecycle of a production language model:

• Pretraining: Training dictionary learning models looks a lot like model pretraining - creating stable, performant training jobs for massively parameterized models across thousands of chips

• Inference: Interp runs a customized inference stack. Day-to-day analysis requires services that allow editing a model's internal activations mid-forward-pass - for example, adding a "steering vector"

• Performance: Like all LLM work, we push up against the limits of hardware and software. Rather than squeezing the last 0.1%, we are focused on finding bottlenecks, fixing them and moving ahead given rapidly evolving research and safety mission

The science keeps scaling - and it's now applied directly in safety audits on frontier models, with real deadlines. As our research has matured, engineering and infrastructure have become a bottleneck. Your work will have a direct impact on one of the most important open problems in AI.

Responsibilities:

• Build and maintain the specialized inference and training infrastructure that powers interpretability research - including instrumented forward/backward passes, activation extraction, and steering vector application

• Resolve scaling and efficiency bottlenecks through profiling, optimization, and close collaboration with peer infrastructure teams

• Design tools, abstractions, and platforms that enable researchers to rapidly experiment without hitting engineering barriers

• Help bring interpretability research into production safety audits - with real deadlines and high reliability expectations

• Work across the stack - from model internals and accelerator-level optimization to user-facing research tooling

You may be a good fit if you:

• Have 5-10+ years of experience building software

• Are highly proficient in at least one programming language (e.g., Python, Rust, Go, Java) and productive with Python

• Are extremely curious about unfamiliar domains; can quickly learn and put that knowledge to work, e.g. diving into new layers of the stack to find bottlenecks

• Have a strong ability to prioritize the most impactful work and are comfortable operating with ambiguity and questioning assumptions

• Prefer fast-moving collaborative projects to extensive solo efforts

• Are curious about interpretability research and its role in AI safety (though no research experience is required!)

• Care about the societal impacts and ethics of your work

• Are comfortable working closely with researchers, translating research needs into engineering solutions.

Strong candidates may also have experience with:

• Optimizing the performance of large-scale distributed systems

• Language modeling fundamentals with transformers

• High Performance LLM optimization: memory management, compute efficiency, parallelism strategies, inference throughput optimization

• Working hands-on in a mainstream ML stack - PyTorch/CUDA on GPUs or JAX/XLA on TPUs

• Collaborating closely with researchers and building tooling to support research teams; or directly performed research with complex engineering challenges

Representative Projects:

• Building Garcon, a tool that allows researchers to easily instrument LLMs to extract internal activations

• Designing and optimizing a pipeline to efficiently collect petabytes of transformer activations and shuffle them

• Profiling and optimizing ML training jobs, including multi-GPU parallelism and memory optimization

• Building a steered inference system that applies targeted interventions to model internals at scale (conceptually similar to Golden Gate Claude but for safety research)

Role Specific Location Policy:

• This role is based in the San Francisco office; however, we are open to considering exceptional candidates for remote work on a case-by-case 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: $315,000-$560,000 USD

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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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You'll be working on large-scale ML systems from the ground up, making safe, steerable, trustworthy systems. You'll be excited to write code when you understand the research context and more broadly why it's important.

Strong candidates may also have experience with high performance, large-scale ML systems, GPUs, Kubernetes, Pytorch, or OS internals, language modeling with transformers, reinforcement learning, and large-scale ETL.

Representative projects may include optimizing the throughput of a new attention mechanism, comparing the compute efficiency of two Transformer variants, making a Wikipedia dataset in a format models can easily consume, scaling a distributed training job to thousands of GPUs, writing a design doc for fault tolerance strategies, and creating an interactive visualization of attention between tokens in a language model.

The annual compensation range for this role is $350,000-$500,000 USD.

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You will work closely with our team of researchers, engineers, and policy experts to build beneficial AI systems. Your contributions will have a direct impact on the development of our AI technology and its applications.

We are looking for a highly motivated and experienced engineer who is passionate about solving complex systems problems and has a strong background in software engineering or machine learning. If you are excited about the opportunity to work on cutting-edge AI technology and make a meaningful contribution to the field, we encourage you to apply.

Responsibilities:

• Identify and solve novel systems problems that arise when running machine learning algorithms at scale
• Develop systems that optimize the throughput and robustness of our largest distributed systems
• Collaborate with our team of researchers, engineers, and policy experts to build beneficial AI systems
• Contribute to the development of our AI technology and its applications

Requirements:

• Significant software engineering or machine learning experience, particularly at supercomputing scale
• Results-oriented, with a bias towards flexibility and impact
• Ability to pick up slack, even if it goes outside your job description
• Enjoy pair programming
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Preferred qualifications:

• Experience with high-performance, large-scale ML systems
• GPU/Accelerator programming
• ML framework internals
• OS internals
• Language modeling with transformers

Benefits:

• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Lovely office space in which to collaborate with colleagues

Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process

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We're looking for a Research Scientist to join our Science unit, where you'll be at the forefront of applying generative AI to predict the structure and properties of complex matter.

As a Research Scientist, you'll design and train advanced AI models to model the structure and properties of complex physical systems, develop a deep understanding of scientific domains, and collaborate with other scientists and engineers to shape the research roadmap.

Key responsibilities include designing and executing robust ML experiments, accumulating small improvements, and sharing results through clear verbal and written communication.

To succeed in this role, you'll need a PhD or equivalent experience in computer science, computational chemistry, materials science, physics, or a related field, with fluency in generative models and transformers.

Experience with modern deep learning frameworks and a record of high-impact published work at the intersection of AI and natural science are also advantageous.

Applications close on Monday 20th April at 5pm BST.

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As an Engineering Manager on Anthropic's performance and scaling teams, you will be responsible for ensuring your team identifies and removes bottlenecks, builds robust and durable solutions, and maximizes the efficiency of our systems.

Responsibilities:

• Provide front-line leadership of engineering efforts to improve model performance and scale our inference and training systems
• Become familiar with the team's technical stack enough to make targeted contributions as an individual contributor
• Manage day-to-day execution of the team's work
• Prioritize the team's work and manage projects in a highly dynamic, fast-paced environment
• Coach and support your reports in understanding, and pursuing, their professional growth
• Maintain a deep understanding of the team's technical work and its implications for AI safety

You may be a good fit if you:

• Have 1+ years of management experience in a technical environment, particularly performance or distributed systems
• Have a background in machine learning, AI, or a similar related technical field
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Excel at building strong relationships with stakeholders at all levels
• Are a quick learner, capable of understanding and contributing to discussions on complex technical topics
• Have experience managing teams through periods of rapid growth and change

Strong candidates may also have experience with:

• High-performance, large-scale ML systems
• GPU/Accelerator programming
• ML framework internals
• OS internals
• Language modeling with transformers

The annual compensation range for this role is $500,000-$850,000 USD.

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Working onsite with seasoned engineers and multidisciplinary teams, you'll tackle challenges to ensure BV power distribution and backup systems are reliable, safe, and scalable.

Responsibilities include participating in design meetings, conducting feasibility studies, supporting the design and construction process, ensuring compliance with industry standards and safety regulations, and contributing to troubleshooting power issues.

You'll also assist in managing maintenance schedules, analyzing and forecasting power needs, implementing strategies for energy efficiency and cost reduction, and promoting a safe working environment.

As an EIT, you'll work closely with senior engineers and team members, seeking guidance and contributing to team efforts.

This is a challenging role that requires strong technical skills, excellent communication skills, and a willingness to learn and adapt to new situations.

If you're interested in project management and supporting large-scale data center projects, this could be the ideal opportunity for you.

At xAI, we operate with a flat organisational structure, and all employees are expected to be hands-on and contribute directly to the company's mission.

Leadership is given to those who show initiative and consistently deliver excellence.

Work ethic and strong prioritisation skills are important, and all employees are expected to have strong communication skills.

They should be able to concisely and accurately share knowledge with their teammates.

Basic qualifications include a Bachelor's Degree in Electrical Engineering or a related field, Engineer in Training (EIT) certification, and 2-4 years of relevant experience in data center operations or electrical engineering.

You should have basic knowledge of data center electrical systems, including transformers, switchgear, generators, and UPS systems.

Proficiency with tools like AutoCAD, Revit, Bluebeam, and SKM is also required.

Interest in project management and supporting large-scale data center projects is a plus.

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Collaborating onsite with multidisciplinary teams, you'll drive solutions to ensure our power distribution and backup systems are reliable, safe, and scalable.

Dive into complex technical challenges, mentor junior staff, and deliver innovations that support mission-critical operations, advancing xAI's goal of accelerating human scientific discovery through AI.

Responsibilities:

• Lead design meetings for electrical systems in new data center builds and expansions, providing expert input from an electrical engineering perspective.

• Conduct feasibility studies and develop detailed specifications for system components.

• Oversee the design and construction process, including permitting, collaborating with licensed engineers and stakeholders.

• Manage the installation of power distribution units (PDUs), UPS systems, cooling units, and other critical infrastructure.

• Ensure full compliance with industry standards (e.g., Uptime Institute Tier Standards), safety regulations, and company policies.

• Develop and manage maintenance schedules to ensure electrical systems operate at peak efficiency.

• Lead troubleshooting of power issues, minimising downtime and enhancing system reliability.

• Perform in-depth analysis and forecasting of power needs based on current and projected IT loads.

• Implement strategies for energy efficiency, cost reduction, and sustainability.

• Collaborate with senior engineers and cross-functional teams, providing guidance and driving collective efforts.

• Champion a culture of continuous improvement, innovation, and knowledge sharing within the team.

• Lead project management for infrastructure initiatives, from conception through commissioning.

• Coordinate with external vendors, contractors, and internal departments to deliver projects on time and within budget.

• Verify all work complies with local, state, and federal regulations concerning electrical systems.

• Foster a safe working environment by enforcing and promoting safety protocols.

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We are seeking an Applied AI, AI Engineer to join our customer-facing technical organization. As a member of our team, you will work directly with enterprise clients from pre-sales through implementation to deploy cutting-edge AI solutions that deliver measurable business impact.

Your primary responsibility will be to identify high-value internal use cases across engineering, legal, HR, sales, and operations, and build or vibe code end-to-end LLM applications. You will own the full lifecycle of these applications, from prototype to production, maintenance, and iteration.

In addition to your technical skills, you will also be responsible for documenting learnings and sharing insights with product and research teams, and converting successful internal tools into customer demos or case studies where appropriate.

How We Work in Applied AI

We care about people and outputs. What matters is what you ship, not the time you spend on it. Bureaucracy is where urgency goes to vanish. You talk to whoever you need to talk to. The best idea wins, whether it comes from a principal engineer or someone in their first week. Always ask why. The best solutions come from deep understanding, not from copying what worked before. We say what we mean. Feedback is direct, timely, and given because we care. No politics. Low ego, high standards. We embrace an unstructured environment and find joy in it.

About You

You are fluent in English and have 3+ years of experience building production software, with meaningful experience deploying LLM applications. You have a bias toward shipping, preferring a working prototype over a perfect specification. You possess strong technical coding skills in Python and front-end skills with React Frameworks. You are comfortable working autonomously across teams with different needs and constraints, and have strong communication skills to bridge non-technical teams and AI capabilities.

Ideally, you have contributions to open-source evaluation frameworks or published research on LLM evaluation, experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect, or Technical Product Manager, and experience with ML frameworks (PyTorch, HuggingFace Transformers).

Benefits

PTO: The CDI contract will be a 'Forfait 218 jours', corresponding to 25 days of holidays and on average 8 to 10 days of RTT days, and complete autonomy on working hours.

Health: Full health insurance coverage for you and your family.

Transportation: We offer a €600 annual mobility allowance, covering 50% of your public transportation costs and including the Sustainable Mobility Allowance (FMD), encouraging eco-friendly travel options such as cycling or carpooling.

Food: Swile meal vouchers with 10,83€ per worked day, including 60% offered by the company.

Sport: Gymlib - sponsorship by Mistral of a significant part of the monthly fee (depending on the program you chose).

Parental policy: 4 additional weeks for parents on top of what is offered by the French state.

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The Applied AI team is Mistral's customer-facing technical organization. We work directly with enterprise clients from pre-sales through implementation to deploy cutting-edge AI solutions that deliver measurable business impact.

As a first Evaluation Engineer, you'll design the methodology, build the infrastructure, and define what 'ready for production' means across verticals and use cases. You will design and implement evaluation systems that help our customers understand model performance across their specific use cases, build robust evaluation infrastructure, and work closely with both research and customer-facing teams.

Research builds evals for frontier capabilities but customers don't care about MMLU scores. We need in Applied AI evals and frameworks for customer reality domain-specific, risk-aware, production-grade. The kind that tell you whether your medical summarization model will hallucinate drug interactions, or whether your legal assistant will invent case citations.

This role sits at the intersection of research, engineering, and solutions, you will play a critical cross role in measuring, understanding, and improving the capabilities of our models for our enterprise customers.

Responsibilities

• Design and implement comprehensive evaluation frameworks to measure LLM capabilities across diverse customer use cases, including text generation, reasoning, code, and domain-specific applications

• Build scalable evaluation infrastructure and pipelines that enable rapid, reproducible assessment of model performance

• Develop novel evaluation methodologies to assess emerging capabilities or verticalized use cases (cybersecurity, finance, healthcare, etc.) and enable the Solutions (Deployment Strategist and Applied AI) on these topics

• Create custom evaluation suites tailored to enterprise customers' specific needs, working closely with them to understand their requirements and success criteria

• Collaborate with research teams to translate evaluation insights into model improvements and training decisions

• Partner with product teams to continuously improve our evaluation tooling based on customer feedback

How We Work in Applied AI

• We care about people and outputs

• What matters is what you ship, not the time you spend on it

• Bureaucracy is where urgency goes to vanish. You talk to whoever you need to talk to

• The best idea wins, whether it comes from a principal engineer or someone in their first week

• Always ask why. The best solutions come from deep understanding, not from copying what worked before

• We say what we mean. Feedback is direct, timely, and given because we care

• No politics. Low ego, high standards

• We embrace an unstructured environment and find joy in it

About You

• You are fluent in English

• 3+ years of experience in ML evaluation, benchmarking for LLM or agentic systems

• You have proven experience in AI or machine learning product implementation with APIs, back-end

• You have deep understanding of concepts and algorithms underlying machine learning and LLMs

• You have strong technical coding skills in Python

• You hold strong communication skills with an ability to explain complex technical concepts in simple terms with technical and non-technical audiences

Ideally You Have:

• Contributions to open-source evaluation frameworks (e.g., LM Eval Harness, OpenAI Evals) or published research on LLM evaluation

• Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect or Technical Product Manager

• Experience with ML frameworks (PyTorch, HuggingFace Transformers)

Benefits

• PTO: The CDI contract will be a 'Forfait 218 jours', corresponding to 25 days of holidays and on average 8 to 10 days of RTT days, and complete autonomy on working hours

• Health: Full health insurance coverage for you and your family

• Transportation: We offer a €600 annual mobility allowance. This package covers 50% of your public transportation costs and includes the Sustainable Mobility Allowance (FMD), encouraging eco-friendly travel options such as cycling or carpooling

• Food: Swile meal vouchers with 10,83€ per worked day, incl 60% offered by company

• Sport: Gymlib - sponsorship by Mistral of a significant part of the monthly fee (depending on the program you chose)

• Parental policy: 4 additional weeks for parents on top of what is offered by the French state

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Job Description:

Stripe's mission is to accelerate global economic and technological development. We handle over $1.4T in payments volume per year, which is roughly 1.3% of the world's GDP.

As an AI/ML Engineering Manager, you will oversee three critical teams within PayIntel, driving the strategic direction and execution for how Payments, and Stripe more generally, adopts AI, and how our suite of performance products leverage ML/AI to provide a consistent experience that generates revenue for Stripe.

Responsibilities:

• Lead the development of our decisioning platform, ensuring that ML/AI decisions are consistent across the lifecycle of a payment regardless of which product makes those decisions.
• Collaborate with the ML Foundations team to develop and deploy Stripe's Foundation Model to risk, conversion and growth opportunities in Payments.
• Extend our performance analytics, observability, and risk management capabilities across Stripe so that users have a consistently high quality performance experience for cost, revenue, and risk across Stripe.
• Expand our Payments Analytics solution to ensure that users are fully aware of performance opportunities and can take full advantage of our suite of products to automate improvements.

Requirements:

• 10+ years of experience building and shipping ML models that power AI/ML product features, with a strong emphasis on modern technologies such as DNNs, Transformers, and Foundation Models.
• 5+ years of experience managing and developing a team of managers, fostering their growth and ensuring alignment with strategic objectives.
• A strong builder mentality, with the ability to define a team's charter and lead the development of complex systems from scratch.
• Proven ability to shepherd large, complex projects and drive transformational change in an organization and with partners that depend on your team's platform services.
• Deep passion for solving really interesting problems, willingness to experiment, engage with customers directly to understand how well our solutions are working, and to build deep knowledge about performance that drives impact across the company.

Preferred Qualifications:

• Experience with a large-scale, data-rich product in a domain such as payments, commerce, search, or social media.
• Knowledge of the challenges and opportunities in applying ML to fraud prevention, consumer intelligence, or financial services.
• Experience building platforms that accelerate service adoption outside your organization with little maintainability overhead.

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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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You will work on integrating Hugging Face's open-source libraries like Transformers and Diffusers, with major cloud platforms or managed SaaS solutions. This role involves bridging and integrating models with different cloud providers, ensuring the models meet expected performance, designing and developing easy-to-use, secure, and robust developer experiences and APIs for our users, writing technical documentation, examples and notebooks to demonstrate new features, and sharing and advocating your work and the results with the community.

The ideal candidate will have deep experience building with Hugging Face Technologies, including Transformers, Diffusers, Accelerate, PEFT, Datasets, expertise in Deep Learning Framework, preferably PyTorch, optionally XLA understanding, strong knowledge of cloud platforms like AWS and services like Amazon SageMaker, EC2, S3, CloudWatch and/or Azure and GCP equivalents, experience in building MLOps pipelines for containerizing models and solutions with Docker, familiarity with Typescript, Rust, and MongoDB, Kubernetes are helpful, ability to write clear documentation, examples and definition and work across the full product development lifecycle, and bonus experience with Svelte & TailwindCSS.

We are actively working to build a culture that values diversity, equity, and inclusivity. We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community.

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We are looking for a Cloud Machine Learning engineer responsible to help build machine learning solutions used by millions leveraging cloud technologies. You will work on integrating Hugging Face's open-source libraries like Transformers and Diffusers, with major cloud platforms or managed SaaS solutions.

Responsibilities:

• Bridging and integrating 🤗 transformers/diffusers models with a different Cloud provider.
• Ensuring the above models meet the expected performance
• Designing & Developing easy-to-use, secure, and robust Developer Experiences & APIs for our users.
• Write technical documentation, examples and notebooks to demonstrate new features
• Sharing & Advocating your work and the results with the community.

About You You'll enjoy working on this team if you have experience with and interest in deploying machine learning systems to production and build great developer experiences. The ideal candidate will have skills including:

• Deep experience building with Hugging Face Technologies, including Transformers, Diffusers, Accelerate, PEFT, Datasets
• Expertise in Deep Learning Framework, preferably PyTorch, optionally XLA understanding
• Strong knowledge of cloud platforms like AWS and services like Amazon SageMaker, EC2, S3, CloudWatch and/or Azure and GCP equivalents.
• Experience in building MLOps pipelines for containerizing models and solutions with Docker
• Familiarity with Typescript, Rust, and MongoDB, Kubernetes are helpful
• Ability to write clear documentation, examples and definition and work across the full product development lifecycle
• Bonus: Experience with Svelte & TailwindCSS

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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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We'll brainstorm with you to put you in a position to do the work that interests you and that is impactful. 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.

If you love open-source, are passionate about making complex technology more accessible, and want to contribute to one of the fastest-growing ML ecosystems, 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 background complement one another.

More about Hugging Face

We are actively working to build a culture that values diversity, equity, and inclusivity. We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community.

Requirements

Please provide a cover letter mentioning why you would like to work in open-source at Hugging Face. We encourage you to mention your skills, potential expertise, and topics on which you would like to work.

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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.

We are a global organisation with teams distributed between France, USA, UK, Germany, and Singapore. Our comprehensive AI platform meets enterprise needs, whether on-premises or in cloud environments.

Our offerings include le Chat, the AI assistant for life and work.

About The Job

The Applied AI team is Mistral's customer-facing technical organisation. We work directly with enterprise clients from pre-sales through implementation to deploy cutting-edge AI solutions that deliver measurable business impact.

As a first Evaluation Engineer, you'll design the methodology, build the infrastructure, and define what 'ready for production' means across verticals and use cases. You will design and implement evaluation systems that help our customers understand model performance across their specific use cases, build robust evaluation infrastructure, and work closely with both research and customer-facing teams.

Research builds evals for frontier capabilities but customers don't care about MMLU scores. We need in Applied AI evals and frameworks for customer reality domain-specific, risk-aware, production-grade. The kind that tell you whether your medical summarization model will hallucinate drug interactions, or whether your legal assistant will invent case citations.

This role sits at the intersection of research, engineering, and solutions, you will play a critical cross role in measuring, understanding, and improving the capabilities of our models for our enterprise customers.

Responsibilities

• Design and implement comprehensive evaluation frameworks to measure LLM capabilities across diverse customer use cases, including text generation, reasoning, code, and domain-specific applications

• Build scalable evaluation infrastructure and pipelines that enable rapid, reproducible assessment of model performance

• Develop novel evaluation methodologies to assess emerging capabilities or verticalized use cases (cybersecurity, finance, healthcare, etc.) and enable the Solutions (Deployment Strategist and Applied AI) on these topics

• Create custom evaluation suites tailored to enterprise customers' specific needs, working closely with them to understand their requirements and success criteria

• Collaborate with research teams to translate evaluation insights into model improvements and training decisions

• Partner with product teams to continuously improve our evaluation tooling based on customer feedback

How We Work in Applied AI

• We care about people and outputs

• What matters is what you ship, not the time you spend on it

• Bureaucracy is where urgency goes to vanish. You talk to whoever you need to talk to. The best idea wins, whether it comes from a principal engineer or someone in their first week

• Always ask why. The best solutions come from deep understanding, not from copying what worked before

• We say what we mean. Feedback is direct, timely, and given because we care

• No politics. Low ego, high standards

• We embrace an unstructured environment and find joy in it

About You

• You are fluent in English

• 3+ years of experience in ML evaluation, benchmarking for LLM or agentic systems

• You have proven experience in AI or machine learning product implementation with APIs, back-end

• You have deep understanding of concepts and algorithms underlying machine learning and LLMs

• You have strong technical coding skills in Python

Ideally You Have:

• Contributions to open-source evaluation frameworks (e.g., LM Eval Harness, OpenAI Evals) or published research on LLM evaluation

• Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect or Technical Product Manager

• Experience with ML frameworks (PyTorch, HuggingFace Transformers)

Benefits

PTO: The CDI contract will be a 'Forfait 218 jours', corresponding to 25 days of holidays and on average 8 to 10 days of RTT days, and complete autonomy on working hours

Health: Full health insurance coverage for you and your family

Transportation: We offer a €600 annual mobility allowance. This package covers 50% of your public transportation costs and includes the Sustainable Mobility Allowance (FMD), encouraging eco-friendly travel options such as cycling or carpooling

Food: Swile meal vouchers with 10,83€ per worked day, incl 60% offered by company

Sport: Gymlib - sponsorship by Mistral of a significant part of the monthly fee (depending on the program you chose)

Parental policy: 4 additional weeks for parents on top of what is offered by the French state

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Constructor is a U.S. based company that has been in the market since 2019, providing a next-generation platform for search and discovery in e-commerce. Our search engine is entirely invented in-house utilizing transformers and generative LLMs, and we use its core and personalization capabilities to power everything from search itself to recommendations to shopping agents.

About You

As the Product Manager for Searchandizing, you will lead a cross-functional team to transform powerful merchandising tools into a guided, intuitive workflow that empowers customers to curate shopping experiences according to business goals. You are a user experience advocate and empathetic problem solver who can step into the shoes of e-commerce merchandisers to translate complex business needs into ROI-focused solutions.

Job Description

Constructor is the only platform that delivers reinforcement-learning-based product discovery that delivers the right product to the right person at the right time in the right context. However, our customers need to feel in control. They need to adjust shopping experience on top of Constructor algorithms to support their business goals, brand positioning, and marketing strategies. As a Product Manager, you will lead the Searchandizing team in providing customers with advanced tools to flexibly curate shopping experiences that optimize net revenue on top of Constructor's algorithms.

What You Will Do

• Develop and execute a comprehensive product strategy for Searchandizing, ensuring it drives Constructor business by solving for customer needs.
• Own the Searchandizing workflow, from identifying the intent and setting up goals to launching campaigns, reviewing their performance, and iterating.
• Solve the real jobs of e-commerce merchandisers, including launching new products, managing stock levels, and providing smooth shopping experience when landing onsite from external sources.
• Prove ROI of Searchandizing by driving net revenue for our customers.
• Work closely and build trust with customers to understand who are the key users, what is their work process, and what makes them successful.
• Keep track of the competition and deliver clear differentiation of Constructor merchandising controls.
• Develop a holistic vision for all the tools Constructor provides to curate shopping experience (merch rules, facet controls, synonyms, redirects, etc.) and introduce a clear framework for team ownership, structure, and growth.
• Collaborate with multiple product teams to provide holistic dashboard experience and synergy between manual curation and algorithmic optimizations.
• Lead and scale a cross-functional team of engineers and designers, while owning the full product cycle from discovery to prototyping, implementation, launching, enabling fellow teams and customers, and iterating on results.
• Communicate effectively at all levels to ensure wonderful experience for customers, prospects, GTM teams, and internal stakeholders, by setting the right expectations and providing regular updates on product strategy, roadmaps, and progress against key metrics.

Requirements

• You are a user experience advocate with a track record of redesigning fragmented toolsets into single, guided workflows.
• You understand that a feature isn't 'done' until it is intuitive and fits naturally into the user's everyday process.
• You are a problem solver, meaning you dig into root causes to understand that a customer request for 'more rules' can actually mean a better way to launch new products or manage out-of-stock inventory.
• You are focused on ROI and understand that merchandisers' success is measured not only by the amount of control they have but also by the net revenue they create working in tandem with Constructor algorithms, and you want your product to prove that value explicitly.
• You are empathetic to customers, acknowledging they often don't know the right answers and need guidance towards data-driven decisions.
• You are inspired to motivate cross-functional teams by advocating customer needs, managing technical complexity, and delegating responsibility.
• You feel confident about managing commitments and expectations from numerous internal and external stakeholders while ensuring strategic focus.
• You seek a fast-moving entrepreneurial environment and thrive in ever-evolving processes.
• You base decisions on data but are ready to take action when faced with uncertainty.
• You are excited to bring learnings from your experience to augment our product culture and processes.
• You have experience of working remotely and in agile software development with empowered cross-functional teams.

Benefits

• Unlimited vacation time - we strongly encourage all employees to take at least 3 weeks per year.
• Fully remote team - choose where you live.
• Work from home stipend - we want you to have the resources you need to set up your home office.
• Apple laptops provided for new employees.
• Training and development budget - refreshed each year for every employee.
• Maternity & Paternity leave for qualified employees.
• Work with smart people who will help you grow and make a meaningful impact.
• Base salary: $90K - $135K USD, depending on knowledge, skills, experience, and interview results.
• Stock options - offered in addition to the base salary.
• Regular team offsites to connect and collaborate.

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Constructor is a U.S. based company that develops a next-generation platform for search and discovery in ecommerce, built to optimize for metrics like revenue, conversion rate, and profit. Our search engine is entirely invented in-house utilizing transformers and generative LLMs, and we use its core and personalization capabilities to power everything from search itself to recommendations to shopping agents.

About the Team

The Ranking team, within the Machine Learning chapter, plays a central role in implementing algorithms that optimize our customers' business KPIs like revenue and conversion rates. We focus on metrics over features, supplying our ranking algorithms with powerful capabilities that bring value to our customers.

Role Details

Design and Develop ML-Based Ranking Solutions

As a Machine Learning Engineer on the Ranking team, your primary focus will be to enhance the quality of our ranking systems, ensuring that search, browse, and autocomplete experiences are highly relevant, personalized, and diverse. You will work on building state-of-the-art ranking algorithms that improve user experience and drive critical business metrics such as conversion, user engagement, and revenue growth.

Improve Ranking Quality

You will analyze ranking performance and identify gaps in search, browse, and autocomplete experiences, focusing on relevance, personalization, attractiveness, diversification, and other quality signals.

Innovate and Optimize Ranking Algorithms

You will proactively propose new machine learning models, algorithms, and features to advance the ranking pipeline, improve ranking quality, and meet evolving business needs.

Collaboration with Cross-Functional Teams

You will collaborate with technical and non-technical business partners to develop / update ranking functionalities (both within and outside the team)

Requirements

Hard Skills

• At least 4 years of experience with Python for machine learning and backend development
• At least 4 years of experience developing, deploying, and maintaining machine learning models with a strong focus on ranking systems for search, recommendations, or similar applications
• Experience in large-scale ML model training, evaluation, and optimization, with a focus on real-time inference and serving
• Experience with big data frameworks such as Spark for processing large datasets and integrating them into ML pipelines
• Proficiency in using tools like SQL, PySpark, Pandas, and other frameworks to extract, manipulate, and analyze data
• Experience with data pipeline orchestration tools like Airflow or Luigi to manage and automate workflows for ML training and signal delivery
• Experience working on ranking algorithms that optimize metrics such as relevance, conversion rates, personalization, user engagement, RPV is a plus

Soft Skills

• Experience collaborating in cross-functional teams
• Experience leading projects to success
• Excellent English communication skills
• Enjoy helping others around you grow as developers and be successful
• Pick up new ideas and technologies quickly, love learning and talking to others about them
• Love to experiment and use data and customer feedback to drive decision making

Benefits

• Unlimited vacation time
• Fully remote team
• Work from home stipend
• Apple laptops provided for new employees
• Training and development budget for every employee, refreshed each year
• Maternity & Paternity leave for qualified employees
• Work with smart people who will help you grow and make a meaningful impact
• Base salary: $80k–$120k USD, depending on knowledge, skills, experience, and interview results
• Stock options
• Regular team offsites to connect and collaborate

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You will work 90% remotely and 10% on-site at events, where you will engage with prospects and customers alongside the Constructor team. Our search engine is built to explicitly optimize for metrics like revenue, conversion rate, and profit, using transformers and generative LLMs.

We are seeking self-starters with a passion for both technology and people. You will identify and engage the right prospects within target accounts through high-quality research and thoughtful outreach, focusing on relevance over volume. You will personalize every interaction by deeply understanding a prospect’s industry, role, challenges, and business priorities.

You will initiate meaningful conversations that center on the customer’s needs, pains, and goals - rather than pitching our services upfront. You will act as a trusted first point of contact, representing our values. You will collaborate closely with Enterprise Account Executives and Marketing to ensure alignment on account strategy and messaging.

You will share insights from prospect conversations to help refine our go-to-market approach and improve customer relevance. You will maintain accurate records of all outreach, research, and engagement in Salesforce, ensuring visibility and collaboration across the team. You will continuously experiment with messaging, channels, and approaches to improve resonance and conversion.

Requirements include 1+ year(s) experience in sales or sales development, excellent verbal and written communication in English & German, and basic comfort working with sales tools such as Salesforce, LinkedIn Sales Navigator and Outreach. You will be based in Europe, with the ability to work across EMEA time zones, and have a genuine interest in technology, ecommerce, and helping businesses solve complex challenges.

Benefits include unlimited vacation time, a competitive compensation package including stock options, fully remote team, work from home stipend, Apple laptops provided for new employees, training and development budget for every employee, refreshed each year, maternity & paternity leave for qualified employees, and work with smart people who will help you grow and make a meaningful impact.

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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:

Anthropic's performance and scaling teams focus on making the most efficient and impactful use of our compute resources, be it inference or training. As an Engineering Manager on these teams you will be responsible for ensuring you and your team are identifying and removing bottlenecks, building robust and durable solutions, and maximizing the efficiency of our systems. You also will help bring clarity, focus, and context to your teams in a fast paced, dynamic environment.

Responsibilities:

• Provide front-line leadership of engineering efforts to improve model performance and scale our inference and training systems
• Become familiar with the team’s technical stack enough to make targeted contributions as an individual contributor
• Manage day-to-day execution of the team's work
• Prioritize the team’s work and manage projects in a highly dynamic, fast paced environment
• Coach and support your reports in understanding, and pursuing, their professional growth
• Maintain a deep understanding of the team's technical work and its implications for AI safety

You may be a good fit if you:

• Have 1+ years of management experience in a technical environment, particularly performance or distributed systems
• Have a background in machine learning, AI, or a similar related technical field
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Excel at building strong relationships with stakeholders at all levels
• Are a quick learner, capable of understanding and contributing to discussions on complex technical topics
• Have experience managing teams through periods of rapid growth and change
• Are a quick study: this team sits at the intersection of a large number of different complex technical systems that you’ll need to understand (at a high level of abstraction) to be effective

Strong candidates may also have experience with:

• High performance, large-scale ML systems
• GPU/Accelerator programming
• ML framework internals
• OS internals
• Language modeling with transformers

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 our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and paid time off, and a comprehensive benefits package.

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As a TPU Kernel Engineer, you'll be responsible for identifying and addressing performance issues across many different ML systems, including research, training, and inference. A significant portion of this work will involve designing and optimising kernels for the TPU. You will also provide feedback to researchers about how model changes impact performance.

You may be a good fit if you:

• Have significant experience optimising ML systems for TPUs, GPUs, or other accelerators
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High performance, large-scale ML systems
• Designing and implementing kernels for TPUs or other ML accelerators
• Understanding accelerators at a deep level, e.g. a background in computer architecture
• ML framework internals
• Language modeling with transformers

Representative projects:

• Implement low-latency, high-throughput sampling for large language models
• Adapt existing models for low-precision inference
• Build quantitative models of system performance
• Design and implement custom collective communication algorithms
• Debug kernel performance at the assembly level

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 our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

Guidance on Candidates' AI Usage:

Learn about our policy for using AI in our application process

Apply for this job

• indicates a required field

First Name Last Name Email Country Phone* 244 results found No results found

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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

We are looking for ML Engineers and Research Engineers to help detect and mitigate misuse of our AI systems. As a member of the Safeguards ML team, you will build systems that identify harmful use—from individual policy violations to sophisticated, coordinated attacks—and develop defenses that keep our products safe as capabilities advance. You will also work on systems that protect user wellbeing and ensure our models behave appropriately across a wide range of contexts. This work feeds directly into Anthropic's Responsible Scaling Policy commitments.

Responsibilities

• Develop classifiers to detect misuse and anomalous behavior at scale. This includes developing synthetic data pipelines for training classifiers and methods to automatically source representative evaluations to iterate on
• Build systems to monitor for harms that span multiple exchanges, such as coordinated cyber attacks and influence operations, and develop new methods for aggregating and analyzing signals across contexts
• Evaluate and improve the safety of agentic products—developing both threat models and environments to test for agentic risks, and developing and deploying mitigations for prompt injection attacks
• Conduct research on automated red-teaming, adversarial robustness, and other research that helps test for or find misuse

You may be a good fit if you

• Have 4+ years of experience in ML engineering, research engineering, or applied research, in academia or industry
• Have proficiency in Python and experience building ML systems
• Are comfortable working across the research-to-deployment pipeline, from exploratory experiments to production systems
• Are worried about misuse risks of AI systems, and want to work to mitigate them
• Have strong communication skills and ability to explain complex technical concepts to non-technical stakeholders

Strong candidates may also have experience with

• Language modeling and transformers
• Building classifiers, anomaly detection systems, or behavioral ML
• Adversarial machine learning or red-teaming
• Interpretability or probes
• Reinforcement learning
• High-performance, large-scale ML systems

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 our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional

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Running machine learning (ML) algorithms at our scale often requires solving novel systems problems. As a Performance Engineer, you'll be responsible for identifying these problems, and then developing systems that optimize the throughput and robustness of our largest distributed systems. Strong candidates here will have a track record of solving large-scale systems problems and will be excited to grow to become an expert in ML also.

You may be a good fit if you:

• Have significant software engineering or machine learning experience, particularly at supercomputing scale
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High performance, large-scale ML systems
• GPU/Accelerator programming
• ML framework internals
• OS internals
• Language modeling with transformers

Representative projects:

• Implement low-latency high-throughput sampling for large language models
• Implement GPU kernels to adapt our models to low-precision inference
• Write a custom load-balancing algorithm to optimize serving efficiency
• Build quantitative models of system performance
• Design and implement a fault-tolerant distributed system running with a complex network topology
• Debug kernel-level network latency spikes in a containerized environment

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 our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

Guidance on Candidates' AI Usage:

Learn about our policy for using AI in our application process

Apply for this job

• indicates a required field

First Name Last Name Email Country Phone* 244 results found No results found

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Anthropic's performance and scaling teams focus on making the most efficient and impactful use of our compute resources, be it inference or training. As an Engineering Manager on these teams, you will be responsible for ensuring you and your team are identifying and removing bottlenecks, building robust and durable solutions, and maximizing the efficiency of our systems. You also will help bring clarity, focus, and context to your teams in a fast-paced, dynamic environment.

Responsibilities:

• Provide front-line leadership of engineering efforts to improve model performance and scale our inference and training systems
• Become familiar with the team's technical stack enough to make targeted contributions as an individual contributor
• Manage day-to-day execution of the team's work
• Prioritize the team's work and manage projects in a highly dynamic, fast-paced environment
• Coach and support your reports in understanding, and pursuing, their professional growth
• Maintain a deep understanding of the team's technical work and its implications for AI safety

You may be a good fit if you:

• Have 1+ years of management experience in a technical environment, particularly performance or distributed systems
• Have a background in machine learning, AI, or a similar related technical field
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Excel at building strong relationships with stakeholders at all levels
• Are a quick learner, capable of understanding and contributing to discussions on complex technical topics
• Have experience managing teams through periods of rapid growth and change
• Are a quick study: this team sits at the intersection of a large number of different complex technical systems that you'll need to understand (at a high level of abstraction) to be effective

Strong candidates may also have experience with:

• High performance, large-scale ML systems
• GPU/Accelerator programming
• ML framework internals
• OS internals
• Language modeling with transformers

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.

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 our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave policies, and a dynamic and inclusive work environment.

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San Francisco

Employment Type

Full time

Department

Research

Compensation

• $360K – $440K • 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's Training team is responsible for producing the large language models that power our research, our products, and ultimately bring us closer to AGI. Achieving this goal requires combining deep research into improving our current architecture, datasets and optimization techniques, alongside long-term bets aimed at improving the efficiency and capability of future generations of models. We are responsible for integrating these techniques and producing model artifacts used by the rest of the company, and ensuring that these models are world-class in every respect. Recent examples of artifacts with major contributions from our team include GPT4-Turbo, GPT-4o and o1-mini.

About the Role

As a member of the architecture team, you will push the frontier of architecture development for OpenAI's flagship models, enhancing intelligence, efficiency, and adding new capabilities.

Ideal candidates have a deep understanding of LLM architectures, a sophisticated understanding of model inference, and a hands-on empirical approach. A good fit for this role will be equally happy coming up with a creative breakthrough, investing in strengthening a baseline, designing an eval, debugging a thorny regression, or tracking down a bottleneck.

This role is based in San Francisco. 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:

• Design, prototype and scale up new architectures to improve model intelligence
• Execute and analyze experiments autonomously and collaboratively
• Study, debug, and optimize both model performance and computational performance
• Contribute to training and inference infrastructure

You might thrive in this role if you:

• Have experience landing contributions to major LLM training runs
• Can thoroughly evaluate and improve deep learning architectures in a self-directed fashion
• Are motivated by safely deploying LLMs in the real world
• Are well-versed in the state of the art transformer modifications for efficiency

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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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.

XML job scraping automation by YubHub

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.

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Microsoft AI are looking for a talented Principal Applied Scientist at their New York office. This role sits at the heart of advanced AI at web scale, shaping the future of key systems within Microsoft Ads, Copilot, and beyond. You'll work directly with leadership to deliver scalable solutions that enhance ad relevance and optimize user and advertiser experiences.

About the Role

In this role, the Principal Applied Scientist will design and implement state-of-the-art machine learning models and algorithms that power key systems within Microsoft Ads, Copilot, and beyond. The work will directly impact millions of users and advertisers by delivering scalable solutions that enhance ad relevance and optimize user and advertiser experiences.

Accountabilities

• Develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot.
• Design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation and ad relevance.

The Candidate we're looking for

Experience:

• Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ 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 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Technical skills:

• Proven experience in programming and data analysis skills.
• Proven expertise in the areas of Generative AI, deep learning, Reinforcement learning, transformers or LLM.

Personal attributes:

• 6+ years of experience in developing and deploying large-scale machine learning models.

Benefits

• Competitive salary
• Comprehensive benefits package
• Opportunities for professional growth and development
• Collaborative and dynamic work environment

XML job scraping automation by YubHub

Microsoft AI are looking for a talented Principal Applied Scientist at their Mountain View office. This role sits at the heart of advanced AI at web scale, shaping the future of key systems within Microsoft Ads, Copilot, and beyond. You'll work directly with leadership to deliver scalable solutions that enhance ad relevance and optimize user and advertiser experiences.

About the Role

In this role, the Principal Applied Scientist will design and implement state-of-the-art machine learning models and algorithms that power key systems within Microsoft Ads, Copilot, and beyond. The work will directly impact millions of users and advertisers by delivering scalable solutions that enhance ad relevance and optimize user and advertiser experiences.

Accountabilities

• Develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot.
• Design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation and ad relevance.

The Candidate we're looking for

Experience:

• Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ 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 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Technical skills:

• Proven experience in programming and data analysis skills.
• Proven expertise in the areas of Generative AI, deep learning, Reinforcement learning, transformers or LLM.

Personal attributes:

• Proven experience in developing and deploying large-scale machine learning models.

Benefits

• Competitive salary
• Comprehensive benefits package
• Opportunities for professional growth and development
• Collaborative and dynamic work environment

XML job scraping automation by YubHub

Microsoft AI are looking for a talented Principal Applied Scientist at their Redmond office. This role sits at the heart of advanced AI at web scale, shaping the future of key systems within Microsoft Ads, Copilot, and beyond. You'll design and implement state-of-the-art machine learning models and algorithms that power scalable solutions that enhance ad relevance and optimize user and advertiser experiences.

About the Role

In this role, you will develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot. You will design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation and ad relevance. You will drive experimentation through A/B testing and offline validation to evaluate model performance and refine user behavior predictions. You will build robust data pipelines and frameworks for handling large-scale, high-dimensional datasets to support advanced AI applications. You will stay at the forefront of AI research, incorporating the latest advancements to drive innovation and impact across Microsoft platforms.

Accountabilities

• Develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot.
• Design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation and ad relevance.
• Drive experimentation through A/B testing and offline validation to evaluate model performance and refine user behavior predictions.
• Build robust data pipelines and frameworks for handling large-scale, high-dimensional datasets to support advanced AI applications.

The Candidate we're looking for

Experience:

• Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ 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 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Technical skills:

• Proven experience in programming and data analysis skills.
• Proven expertise in the areas of Generative AI, deep learning, Reinforcement learning, transformers or LLM.

Personal attributes:

• Strong problem-solving skills and ability to work independently.
• Excellent communication and collaboration skills.

Benefits

• Competitive salary.
• Comprehensive benefits package.
• Opportunities for professional growth and development.
• Collaborative and dynamic work environment.

XML job scraping automation by YubHub

Microsoft are looking for a talented Senior Applied Scientist at their Mountain View office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising artificial intelligence technology. You'll work directly with leadership to shape the company's direction in the technology market.

About the Role

As a Senior Applied Scientist, you will design and implement cutting-edge machine learning models and algorithms that power relevance systems across all surfaces for Microsoft Ads and Shopping including Bing, Copilot, and beyond. You will have a direct impact on millions of users and advertisers, delivering scalable solutions to enhance ad relevance and optimize user experiences.

Accountabilities

• Own high-impact and open-ended relevance problem areas across Product Ads and Shopping including providing strategic direction to solve problems and applying deep subject matter knowledge to support business impact.
• Drive algorithmic and modeling improvements to the system using primarily deep learning techniques from NLP and computer vision, including latest LLM models, to deliver clear and measurable product impact.

The Candidate we're looking for

Experience:

• Bachelor's Degree in Data Science, Machine Learning, Statistics, Computer Science or Computer Engineering or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Data Science, Machine Learning, Statistics, Computer Science or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Data Science, Machine Learning, Statistics, Computer Science or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Technical skills:

• Data Science, Machine Learning, Statistics, Computer Science or Computer Engineering or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
• 4+ years working experience in statistical natural language processing (NLP) with the latest deep learning technologies including transformer and LLMs OR 4+ years working experience in Computer Vision (CV) with latest deep learning technologies including Vision Transformers.
• 4+ years working experience with coding in production systems using C++, C#, Java or Python.

Personal attributes:

• Strong technical judgment on metrics, evaluation strategies, and tradeoffs optimizing for overall product ROI rather than isolated metrics.
• Ability to operate with high independence and accountability, anticipating risks, planning for unknowns, and requiring minimal oversight to deliver sustained impact at scale.

Benefits

• Competitive salary
• Comprehensive benefits package
• Opportunities for professional growth and development
• Collaborative and dynamic work environment
• Recognition and rewards for outstanding performance

XML job scraping automation by YubHub

Microsoft AI are looking for a talented Senior Applied Scientist at their Redmond office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising artificial intelligence technology. You'll work directly with leadership to shape the company's direction in the AI and machine learning markets.

About the Role

As a Senior Applied Scientist, you will design and implement cutting-edge machine learning models and algorithms that power relevance systems across all surfaces for Microsoft Ads and Shopping including Bing, Copilot, and beyond. You will have a direct impact on millions of users and advertisers, delivering scalable solutions to enhance ad relevance and optimize user experiences. This role is part of Microsoft Artificial Intelligence (MAI)-Ads Engineering and is responsible for the end-to-end relevance problem for our ads and shopping products.

Accountabilities

• Own high-impact and open-ended relevance problem areas across Product Ads and Shopping including providing strategic direction to solve problems and applying deep subject matter knowledge to support business impact.
• Drive algorithmic and modeling improvements to the system using primarily deep learning techniques from NLP and computer vision, including latest LLM models, to deliver clear and measurable product impact.

The Candidate we're looking for

Experience:

• Bachelor's Degree in Data Science, Machine Learning, Statistics, Computer Science or Computer Engineering or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Data Science, Machine Learning, Statistics, Computer Science or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Data Science, Machine Learning, Statistics, Computer Science or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Technical skills:

• Data Science, Machine Learning, Statistics, Computer Science or Computer Engineering or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
• 4+ years working experience in statistical natural language processing (NLP) with the latest deep learning technologies including transformer and LLMs OR 4+ years working experience in Computer Vision (CV) with latest deep learning technologies including Vision Transformers.
• 4+ years working experience with coding in production systems using C++, C#, Java or Python.

Personal attributes:

• Strong technical judgment and problem-solving skills.
• Excellent communication and collaboration skills.
• Ability to work independently and as part of a team.

Benefits

• Competitive salary range: $119,800 - $234,700 per year.
• Comprehensive benefits package, including medical, dental, and vision insurance.
• 401(k) matching program.
• Paid time off and holidays.
• Opportunities for professional growth and development.

XML job scraping automation by YubHub

Microsoft are looking for a talented Senior Applied Scientist at their Redmond office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising the advertising technology space. You'll work directly with leadership to shape the company's direction in the online advertising market.

About the Role

In this role, the Senior Applied Scientist will design and implement state-of-the-art machine learning models and algorithms that power key systems within Microsoft Ads, Microsoft Audience Network, Copilot, and beyond. Your work will directly impact millions of users and advertisers by delivering scalable solutions that enhance ad relevance and optimize user experiences.

Accountabilities

• Develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot.
• Design scalable algorithms for online and offline systems, delivering innovative solutions for content selection, user engagement modeling, and ad generation.

The Candidate we're looking for

Experience:

• Bachelor’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 Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Technical skills:

• Proven experience in programming and data analysis skills 2+ years of experience in developing and deploying large-scale machine learning models.
• Proven expertise in the areas of Generative AI, deep learning, or transformers.

Personal attributes:

• Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.

Benefits

• 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.

XML job scraping automation by YubHub

Microsoft are looking for a talented Senior Applied Scientist at their Mountain View office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising the advertising space. You'll work directly with leadership to shape the company's direction in the online advertising market.

About the Role

In this role, the Senior Applied Scientist will design and implement state-of-the-art machine learning models and algorithms that power key systems within Microsoft Ads, Microsoft Audience Network, Copilot, and beyond. Your work will directly impact millions of users and advertisers by delivering scalable solutions that enhance ad relevance and optimize user experiences.

Accountabilities

• Develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot.
• Design scalable algorithms for online and offline systems, delivering innovative solutions for content selection, user engagement modeling, and ad generation.

The Candidate we're looking for

Experience:

• Bachelor’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 Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Technical skills:

• Proven experience in programming and data analysis skills 2+ years of experience in developing and deploying large-scale machine learning models.
• Proven expertise in the areas of Generative AI, deep learning, or transformers.

Personal attributes:

• Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.

Benefits

• 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.

XML job scraping automation by YubHub

Microsoft AI are looking for a talented Senior Applied Scientist at their Mountain View office. This role sits at the heart of advanced AI at web scale, shaping the future of key systems within Microsoft Ads, Copilot, and beyond. You'll design and implement state-of-the-art machine learning models and algorithms that power scalable solutions that enhance ad relevance and optimize user and advertiser experiences.

About the Role

In this role, you will develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot. You will design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation, and ad relevance. You will drive experimentation through A/B testing and offline validation to evaluate model performance and refine user behavior predictions. You will build robust data pipelines and frameworks for handling large-scale, high-dimensional datasets to support advanced AI applications. You will stay at the forefront of AI research, incorporating the latest advancements to drive innovation and impact across Microsoft platforms.

Accountabilities

• Develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot.
• Design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation, and ad relevance.

The Candidate we're looking for

Experience:

• Bachelor’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 Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Technical skills:

• Proven experience in programming and data analysis skills.
• Proven expertise in the areas of Generative AI, deep learning, Reinforcement learning, transformers or LLM.

Personal attributes:

• Strong communication and collaboration skills.
• Ability to work in a fast-paced environment and adapt to changing priorities.

Benefits

• Competitive salary range of $119,800 - $234,700 per year.
• Comprehensive benefits package, including medical, dental, and vision insurance.
• 401(k) matching program.
• Paid time off and holidays.
• Opportunities for professional growth and development.
• Collaborative and dynamic work environment.

XML job scraping automation by YubHub

Microsoft AI are looking for a talented Senior Applied Scientist at their Vancouver office. This role sits at the heart of advanced AI at web scale, shaping the future of key systems within Microsoft Ads, Copilot, and beyond. You'll design and implement state-of-the-art machine learning models and algorithms that power scalable solutions that enhance ad relevance and optimize user and advertiser experiences.

About the Role

As a Senior Applied Scientist, you will be responsible for developing and deploying cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning. You will design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation, and ad relevance. You will drive experimentation through A/B testing and offline validation to evaluate model performance and refine user behavior predictions. You will build robust data pipelines and frameworks for handling large-scale, high-dimensional datasets to support advanced AI applications. You will stay at the forefront of AI research, incorporating the latest advancements to drive innovation and impact across Microsoft platforms.

Accountabilities

• Develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot.
• Design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation, and ad relevance.
• Drive experimentation through A/B testing and offline validation to evaluate model performance and refine user behavior predictions.
• Build robust data pipelines and frameworks for handling large-scale, high-dimensional datasets to support advanced AI applications.

The Candidate we're looking for

Experience:

• 4+ years of experience in statistics, predictive analytics, research, or a related field.

Technical skills:

• Proven expertise in the areas of Generative AI, deep learning, Reinforcement learning, transformers, or LLM.
• 5+ years of experience in developing and deploying large-scale machine learning models.

Personal attributes:

• Strong problem-solving skills and ability to work independently.
• Excellent communication and collaboration skills.

Benefits

• Competitive salary range: CAD $114,400 - CAD $203,900 per year.
• Comprehensive benefits package, including health, dental, and vision insurance.
• Opportunities for professional growth and development.
• Collaborative and dynamic work environment.

XML job scraping automation by YubHub

Microsoft AI are looking for a talented Applied Scientist II at their Redmond office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI market.

About the Role

As an Applied Scientist II, you will be responsible for building and maintaining production machine learning models to generate image and text assets, multimodal representation and predict ad quality. You will find insights and form hypothesis on web-scale data with various machine learning, computer vision, feature engineering, statistical, and data mining techniques. You will design experiments, understand the resulting data, and produce actionable, trustworthy conclusions from them. You will craft and optimize prompts for effective LLM and VLM performance, and wrangle large amounts of data using various tools.

Accountabilities

• Building and maintaining production machine learning models to generate image and text assets, multimodal representation and predict ad quality.
• Finding insights and forming hypothesis on web-scale data with various machine learning, computer vision, feature engineering, statistical, and data mining techniques.

The Candidate we're looking for

Experience:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience.

Technical skills:

• Experience with Large Language Models: Demonstrated experience working with LLMs/VLMs, such as GPT, BERT, or similar models, including knowledge of their strengths, limitations, and capabilities.
• Solid Understanding of NLP and CV: In-depth knowledge of natural language processing (NLP) and computer vision techniques and concepts, including tokenization, semantic analysis, and text generation, multimodal representation, etc.

Personal attributes:

• Experience delivering, scaling, and maintaining highly successful and innovative machine learning products with your fingerprints all over them.
• Experience in parallel or distributed processing, high performance computing, stream computing and SCOPE.

Benefits

• Competitive salary and benefits package.
• Opportunities for professional growth and development.
• Collaborative and dynamic work environment.

XML job scraping automation by YubHub

Microsoft AI are looking for a talented Senior Applied Scientist at their Toronto office. This role sits at the heart of advanced AI at web scale, shaping the future of key systems within Microsoft Ads, Copilot, and beyond. The work will directly impact millions of users and advertisers by delivering scalable solutions that enhance ad relevance and optimize user and advertiser experiences.

About the Role

In this role, the Senior Applied Scientist will design and implement state-of-the-art machine learning models and algorithms that power key systems within Microsoft Ads, Copilot, and beyond. The work will directly impact millions of users and advertisers by delivering scalable solutions that enhance ad relevance and optimize user and advertiser experiences. Responsibilities span the full modeling lifecycle, including training data and labeling strategy, feature and signal design, model development, and rigorous offline and online evaluation. Engineers and applied scientists work closely at the intersection of machine learning, economics, and large-scale systems to deliver high-performance real-time inference and robust experimentation in production.

Accountabilities

• Develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot.
• Design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation and ad relevance.
• Drive experimentation through A/B testing and offline validation to evaluate model performance and refine user behavior predictions.
• Build robust data pipelines and frameworks for handling large-scale, high-dimensional datasets to support advanced AI applications.
• Stay at the forefront of AI research, incorporating the latest advancements to drive innovation and impact across Microsoft platforms.

The Candidate we're looking for

Experience:

• Bachelor’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 Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Technical skills:

• Proven experience in programming and data analysis skills.
• Proven expertise in the areas of Generative AI, deep learning, Reinforcement learning, transformers or LLM.
• 5+ years of experience in developing and deploying large-scale machine learning models.

Personal attributes:

• Strong problem-solving skills and ability to work independently.
• Excellent communication and collaboration skills.
• Ability to work in a fast-paced environment and adapt to changing priorities.

Benefits

• Competitive salary range of CAD $114,400 – CAD $203,900 per year.
• Comprehensive benefits package, including health, dental, and vision insurance.
• Opportunities for professional development and growth within the company.
• Collaborative and dynamic work environment.
• Access to cutting-edge technology and resources.
• Flexible work arrangements, including remote work options.

XML job scraping automation by YubHub

Microsoft AI are looking for a talented Senior Applied Scientist at their Redmond office. This role sits at the heart of advanced AI at web scale, shaping the future of key systems within Microsoft Ads, Copilot, and beyond. You'll design and implement state-of-the-art machine learning models and algorithms that power scalable solutions that enhance ad relevance and optimize user and advertiser experiences.

About the Role

In this role, you will develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot. You will design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation, and ad relevance. You will drive experimentation through A/B testing and offline validation to evaluate model performance and refine user behavior predictions. You will build robust data pipelines and frameworks for handling large-scale, high-dimensional datasets to support advanced AI applications. You will stay at the forefront of AI research, incorporating the latest advancements to drive innovation and impact across Microsoft platforms.

Accountabilities

• Develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot.
• Design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation, and ad relevance.
• Drive experimentation through A/B testing and offline validation to evaluate model performance and refine user behavior predictions.
• Build robust data pipelines and frameworks for handling large-scale, high-dimensional datasets to support advanced AI applications.

The Candidate we're looking for

Experience:

• Bachelor’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 Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Technical skills:

• Proven experience in programming and data analysis skills.
• Proven expertise in the areas of Generative AI, deep learning, Reinforcement learning, transformers or LLM.

Personal attributes:

• Strong problem-solving skills and ability to work independently.
• Excellent communication and collaboration skills.

Benefits

• Competitive salary presumption of $119,800 - $234,700 per year.
• Comprehensive benefits package, including medical, dental, and vision insurance.
• 401(k) matching program.
• Paid time off and holidays.
• Opportunities for professional growth and development.

XML job scraping automation by YubHub

What you'll do

This Senior Data Scientist role will focus on designing and developing advanced AI-driven solutions for game developers and gamers. The ideal candidate will have strong expertise in Reinforcement Learning (RL), Generative AI agents, and Retrieval-Augmented Generation (RAG) to create intelligent gaming tools and experiences.

• Research, design, and implement rewards-based agents using Reinforcement Learning (RL) for testing gaming applications.
• Develop and optimize agents and advanced RAG-based solutions for dynamic game content creation, adaptive player interactions, and enhanced gaming experiences.

What you need

• Strong background in Reinforcement Learning (RL), Generative AI agents, and RAG (Retrieval-Augmented Generation).
• Proficiency in Python and experience with PyTorch or TensorFlow for deep learning.

XML job scraping automation by YubHub

What you'll do

This role involves working closely with AI engineers, game developers, and software engineers to build cutting-edge AI capabilities that enhance game mechanics, player engagement, and content generation.

• Research, design, and implement rewards-based agents using Reinforcement Learning (RL) for testing gaming applications.
• Develop and optimize agents and advanced RAG-based solutions for dynamic game content creation, adaptive player interactions, and enhanced gaming experiences.

What you need

• Strong background in Reinforcement Learning (RL), Generative AI agents, and RAG (Retrieval-Augmented Generation).
• Proficiency in Python and experience with PyTorch or TensorFlow for deep learning.

XML job scraping automation by YubHub