The tasks of the thesis include the analysis of safety-critical thermal runaway scenarios in lithium-ion batteries and the development of a methodology for AI-supported detection, tracking, and evaluation of particles in the venting process. For this purpose, suitable neural network architectures are identified for particle tracking in highly dynamic and optically challenging environments, and their performance is evaluated in comparison to established image-based methods such as PIV. Based on experimental high-speed recordings, relevant physical parameters are derived and their validity for characterising the thermal runaway event is assessed. The work typically includes the following focal points:

Literature review on particle tracking procedures, image-based flow analysis, and AI methods in the context of safety-critical battery scenarios (e.g., neural networks, deep learning for computer vision, tracking algorithms). Analysis of the physical and measurement-related boundary conditions at thermal runaway of lithium-ion batteries, particularly the gas particle emission and the associated thermal, mechanical, and abrasive stresses. Design, implementation, and training of a neural network for detection and tracking of particles in high-speed recordings of the venting process. Evaluation and validation of the developed approach using experimental data and comparison with classical PIV methods regarding accuracy, robustness, and validity. Derivation of characteristic parameters for qualitative and quantitative description of particle emission during thermal runaway. Documentation and scientific preparation of the results in the framework of a bachelor's or master's thesis with strong practical relevance.

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The goal of this work is to develop a predictive method for evaluating material performance under thermal runaway conditions due to escaping gas and particles at materials used in venting structures. To achieve this, a neural network will be designed, trained, and applied to new materials. The neural network will be trained and validated using existing experimental and simulation data. The generated evaluation data will be compared with results from classical substitute and cell tests to evaluate the performance and reliability of the developed approach. Based on the obtained data, characteristic parameters for evaluating material failure under thermal runaway conditions will be identified and derived.

In the first step, a systematic literature review will be conducted on existing experimental, analytical, and simulation methods for evaluating fire protection materials in the thermal runaway context. Based on this, a suitable model for evaluating material performance in the context of thermal runaway will be developed, trained, and implemented. The model will be validated using experimental data to assess its predictive accuracy and robustness. Finally, the applicability of the developed approach and potential opportunities for further development will be critically discussed.

Key tasks:

• Conduct a systematic literature review on existing experimental, analytical, and simulation methods for evaluating fire protection materials in the thermal runaway context.

• Design, train, and implement a neural network for predictive evaluation of material performance under thermal runaway conditions.

• Evaluate and analyse experimental and simulation data to validate the model.

• Compare generated evaluation data with results from classical substitute and cell tests.

• Identify and derive characteristic parameters for evaluating material failure under thermal runaway conditions.

• Critically discuss the applicability of the developed approach and potential opportunities for further development.

Requirements:

• Bachelor's or master's degree in computer science, mechanical engineering, electrical engineering, or a related field.

• Experience in machine learning and deep learning.

• Familiarity with Python programming language and relevant libraries.

• Good understanding of thermal runaway phenomena and fire protection materials.

• Excellent communication and teamwork skills.

• Ability to work independently and manage multiple tasks.

Preferred skills:

• Experience with neural networks and deep learning frameworks such as TensorFlow or PyTorch.

• Familiarity with simulation software such as ANSYS or COMSOL.

• Knowledge of thermal analysis and heat transfer.

• Experience with data analysis and visualisation tools such as Matplotlib or Seaborn.

• Familiarity with version control systems such as Git.

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Responsibilities

• Research and develop new methods for simulating sensor data
• Collaborate with other teams to integrate sensor simulations into production processes
• Analyze and optimize sensor simulation algorithms for improved accuracy and efficiency
• Develop and maintain documentation for sensor simulation methods and tools
• Present research findings and results to internal stakeholders

Requirements

• PhD in Computer Science, Mathematics, or related field
• Strong background in machine learning and deep learning
• Experience with computer vision and image processing
• Proficiency in Python and C++ programming languages
• Excellent communication and collaboration skills

Nice to Have

• Experience with Gaussian splatting and other sensor simulation techniques
• Familiarity with popular machine learning frameworks such as TensorFlow and PyTorch
• Experience with Linux operating system and Git version control

Benefits

• Competitive salary and benefits package
• Opportunity to work with a leading automotive manufacturer
• Collaborative and dynamic work environment
• Professional development opportunities

If you're passionate about research and development, and want to contribute to the creation of innovative sensor simulation techniques, we encourage you to apply for this exciting opportunity!

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The goal of the team is to build a unified set of risk data for decision-makers at the firm level to make informed decisions about the firm's complex set of positions. The team will be coordinating with multiple different asset-class risk teams to build the firm's high-level view, including building out individual asset-class risk analytics in cases where it is deemed necessary.

This role involves research into using many different statistical and probabilistic techniques to evolve the firm's understanding of risk. Key to the role will be understanding the ways in which different market structures impact their individual asset classes, the behavior of large market participants, shared traits of popular trading strategies, and developing probabilistic methodologies to anticipate potential stress scenarios.

Responsibilities:

• Build and validate cross-asset risk measures.
• Identify market factors across asset classes and identify common risk premia trades.
• Apply feature discovery and classification-style ML to identify and interpret portfolio/trade drivers with careful validation and robustness testing.
• Partner closely with asset class risk teams to test assumptions, interpret results, and drive adoption of the analytics.
• Develop forward-looking scenario models, identifying risks in the firm shared across asset classes.

Requirements:

• 1–5 years of hands-on experience in quantitative research, modeling, or applied ML
• Strong foundation in applied mathematics / statistics / machine learning (especially probability theory, linear algebra, calculus, and statistics)
• Demonstrated ability to design, implement, and validate models from scratch (not just apply off-the-shelf packages)
• Python proficiency for research prototyping and analysis
• Experience with deep learning frameworks (For example, PyTorch/TensorFlow)
• Strong research habits: hypothesis formation, experimentation, back testing/validation, and clear communication
• Financial markets experience is helpful but not required

The estimated base salary range for this position is $175,000 to $250,000, which is specific to New York and may change in the future.

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In this role, you will drive an impactful gene editing program by identifying gene editing targets and alleles that result in desired phenotypic impact, working with a diverse set of data inputs to develop genotype-to-phenotype predictions using some of the industry's most extensive and global agriculture and genetic datasets.

As a member of the Biology and Genome Design community, you will actively build your own acumen and capabilities while modeling best practices for others and partnering closely with cross-functional scientific, engineering, and IT teams across the company.

Responsibilities

• Produce and document recommendations for alleles that deliver desired phenotypic impact, contributing to innovative gene editing strategies
• Develop and apply state-of-the-art genetic discovery tools to identify novel genetics with predicted optimal phenotypic performance by leveraging diverse genomic and phenotypic datasets
• Develop robust workflows and analysis pipelines that enable cross-team communication, data sharing, and decision-making, supporting iterative learning from key experiments to shape research direction
• Identify and refine methods for capturing complex genetic interactions and the influence of genetic variation on observed phenotypes to improve design and prediction for current and future gene editing pipelines
• Lead or assist in the gathering, curation, quality control, and analysis of new data sources (e.g., functional genomics, phenomics) across Research and Development (R&D) teams
• Collaborate closely with cross-functional and cross-cultural scientific, engineering, and IT partners to align gene editing and data science approaches with pipeline and business needs
• Influence key stakeholders by clearly communicating data-driven insights, challenges, and opportunities to facilitate solutions to complex scientific and technical problems

Requirements

• PhD in Biological Sciences, Computational Biology, or a related field
• At least 3 years of post-PhD experience working in gene editing, functional genomics, or closely related fields
• Demonstrated ability to collect, analyze, and leverage complex biological datasets and translate them into testable hypotheses
• Experience with advanced computational and analytical tools in molecular biology, genetics, and biochemistry
• Distinct communication skills with fluency in English, both written and verbal

Preferred Qualifications

• Educational preparation or applied experience in at least one of the following areas: Plant Biology, Genetics, Molecular Biology, Machine/Deep Learning, or other closely related discipline
• Demonstrated experience working collaboratively in cross-functional and cross-cultural teams to achieve common goals
• Ability to influence key stakeholders by articulating challenges and opportunities to drive solutions to complex scientific and organizational challenges
• Results-oriented mindset with demonstrated ability to prioritize and advance multiple projects at the enterprise level

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

Key responsibilities include:

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

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

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

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

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

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

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

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

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

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

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

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

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

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As a Research Scientist Manager, you will lead a world-class team of research scientists and engineers, defining the research roadmap and driving execution from early prototyping to deployment. You'll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.

Key responsibilities include:

• Leading, mentoring, and growing a team of research scientists and engineers working on GenAI research initiatives
• Defining and driving a multi-year research roadmap, identifying key scientific questions, setting milestones, allocating resources, and ensuring rigorous execution
• Collaborating cross-functionally with engineering, product, client-facing teams, and external academic or industry partners to translate research into components, insights, and actionable outcomes
• Communicating compellingly, publishing research, presenting at conferences, engaging in open-source contributions, and representing the team externally
• Driving an inclusive, high-performing culture, helping your team through technical challenges, providing growth opportunities, and attracting top talent

Ideal candidates will have:

• 5+ years of hands-on research experience in machine learning, deep learning, generative models, agent/RL systems, or related domains
• A strong track record of research excellence, including publications in top-tier ML/AI venues
• Experience leading or managing research teams, mentoring, coaching, and developing talent
• Excellent written and verbal communication skills, articulating research ideas and outcomes to technical and non-technical stakeholders

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This high-impact role will drive the technical direction and oversight for Deep Research Agent Development, translating cutting-edge research in Generative AI, Large Language Models (LLMs), and Agentic Frameworks into robust, scalable, and high-impact production systems that enhance enterprise operations, analytics, and core efficiency.

The ideal candidate thrives in a fast-paced environment, has a passion for both deep technical work and mentoring, and is capable of setting a long-term technical strategy for a critical domain while maintaining a strong, hands-on delivery focus.

Responsibilities

Technical Leadership & Vision

• Set the Technical Roadmap: Define and own the technical strategy, architecture, and roadmap for Deep Research Agents for the Enterprise, ensuring alignment with Scale AI’s overall AI strategy and business goals.

• Drive Breakthrough Research to Production: Lead the end-to-end development, from initial research to production deployment, to landing on customer impact, with a focus on integrating diverse data modalities.

• Core Agent Capabilities Development:

Advanced Knowledge Retrieval: Architect and implement state-of-the-art retrieval systems to ensure the agents provide accurate and comprehensive answers from public and proprietary data sources from enterprises.

Data Analysis: Design and champion the development of data analysis agents that accurately translate complex natural language queries into executable SQL/code against diverse enterprise data schemas.

Multimodal Intelligence: Lead the integration of Multimodal AI capabilities to process and extract structured information from visual documents, tables, and forms, enriching the agent's knowledge base.

Architecture & Design: Design and champion highly scalable, reliable, and low-latency infrastructure and frameworks for building, orchestrating, and evaluating multi-agent systems at enterprise scale.

Technical Excellence: Serve as the technical authority for the team, leading design reviews, defining ML engineering best practices, and ensuring code quality, security, and operational excellence for all agent systems.

Team Leadership & Mentorship

• Lead and Mentor: Technically lead and mentor a team of Machine Learning Engineers and Research Scientists, fostering a culture of innovation, rigorous engineering, rapid iteration, and technical depth.

• Recruiting & Growth: Partner with management to hire, onboard, and grow top-tier talent, helping to shape the long-term structure and capabilities of the team.

• Cross-Functional Influence: Collaborate effectively with Product Managers, Data Scientists, and other engineering/science teams to translate ambiguous, high-level business problems into concrete, executable technical specifications and impactful agent solutions.

Basic Qualifications

• Bachelor's degree in Computer Science, Electrical Engineering, a related field, or equivalent practical experience.

• 8+ years of experience in software development, with at least 6 years focused on Machine Learning, Deep Learning, or Applied Research in a production environment.

• 2+ years of experience in a formal or informal Technical Leadership role (Team Lead, Tech Lead) with a focus on setting technical direction for a domain.

• Deep expertise in Generative AI and Large Language Models (LLMs).

• Demonstrated experience designing, building, and deploying AI Agents or complex Agentic systems in production at scale.

• Experience with large-scale distributed systems and real-time data processing.

Preferred Qualifications

• Advanced degree (Master's or Ph.D.) in Computer Science, Machine Learning, or a related quantitative field.

• Demonstrated experience designing and deploying production-grade Text-to-SQL systems, including handling complex schema linking and query optimization.

• Practical experience with Multimodal AI, specifically integrating OCR and vision-language models for document intelligence and structured data extraction from images/forms.

• Proven experience in one or more relevant deep research areas: Reinforcement Learning (RL), Reasoning and Planning, Agentic Systems.

• Experience with vector databases and advanced retrieval techniques.

• A track record of publishing research papers in top-tier ML/AI conferences (e.g., NeurIPS, ICML, ICLR, KDD).

• Excellent written and verbal communication skills, with the ability to articulate complex technical vision to executive stakeholders and technical peers.

• Experience driving cross-team technical initiatives that have delivered significant business impact.

Compensation

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

About Us

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

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Key Responsibilities:

• Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities.
• Design and experiment new approaches to preference optimization.
• Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness.
• Publish research findings in top-tier AI conferences.

Ideal Candidate:

• Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field.
• Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.
• Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning.
• Excellent written and verbal communication skills
• Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals
• Previous experience in a customer-facing role.

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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Your mission is threefold:

• Frontier Research & Publication: Leading research into LLM/agent capabilities, reasoning, and safety, with the goal of publishing at top-tier venues (NeurIPS, ICML, ICLR).
• Cross-Org Impact: Developing generalised techniques in Agent design, AI Safety and Deep Research agents that scale across our commercial and government platforms.
• Mission-Critical Applications: Engineering high-stakes AI systems that impact millions of citizens globally.

You will:

• Pioneer Novel Architectures: Design and train state-of-the-art models and agents, moving beyond “off-the-shelf” solutions to create custom architectures for complex public sector reasoning tasks.
• Lead AI Safety Initiatives: Research and implement robust safety frameworks, including red teaming, alignment (RLHF/DPO), and bias mitigation strategies essential for sovereign AI.
• Drive Deep Research Capabilities: Develop agents capable of long-horizon reasoning and autonomous information synthesis to solve complex problems for national security and public policy.
• Publish and Contribute: Represent Scale in the broader research community by publishing high-impact papers and contributing to open-source breakthroughs.
• Consult as a Subject Matter Expert: Act as a technical authority for public sector leaders, advising on the theoretical limits and safety requirements of emerging AI.
• Build Evaluation Frontiers: Create new benchmarks and evaluation protocols that define what success looks like for high-stakes, non-commercial AI applications.

Ideally, you’d have:

• Advanced Degree: PhD or Master’s in Computer Science, Mathematics, or a related field with a focus on Deep Learning.
• Research Track Record: A portfolio of first-author publications at major conferences (NeurIPS, ICML, CVPR, EMNLP, etc.).
• Engineering Rigour: Strong proficiency in Python, deep learning frameworks (PyTorch/JAX), with the ability to write production-ready code that scales.
• Safety Expertise: Experience in alignment, robustness, or interpretability research.

Nice to haves:

• Experience with large-scale distributed training on massive clusters.
• Experience in building agentic systems that are reliable.
• Experience in Sovereign AI or working with highly regulated data environments.
• A zero-to-one mindset: Comfortable navigating ambiguity and defining research directions from scratch.

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As Director of Enterprise ML, you will lead a world-class team of research scientists and engineers, define the research roadmap, and drive execution from early prototyping to deployment. You’ll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.

This role is ideal for a leader who thrives in ambiguity, understands both frontier GenAI capabilities and their limitations, and is motivated by turning research into durable, production-ready systems.

Key responsibilities include:

• Leading, mentoring, and growing a team of research scientists and engineers working on GenAI research initiatives
• Defining and driving a multi-year research roadmap, identifying key scientific questions, setting milestones, allocating resources, and ensuring rigorous execution
• Collaborating cross-functionally with engineering, product, client-facing teams, and external academic or industry partners to translate research into components, insights, and actionable outcomes
• Communicating compellingly, publishing research, presenting at conferences, engaging in open-source contributions, and representing the team externally
• Driving an inclusive, high-performing culture, helping your team through technical challenges, providing growth opportunities, and attracting top talent

Core qualifications include:

• 8+ years of hands-on research experience in machine learning, deep learning, generative models, agent/RL systems, or related domains
• A strong track record of research excellence, including publications in top-tier ML/AI venues
• Experience leading or managing research teams
• Excellent written and verbal communication skills

Nice-to-have qualifications include:

• Hands-on experience building and deploying agent-based, tool-augmented, or workflow-driven LLM systems in enterprise environments
• Prior ownership of enterprise AI platforms, internal ML products, or customer-facing AI services at scale
• Proven track record of partnering directly with enterprises to identify high-impact use cases and deliver measurable business outcomes

Compensation packages at Scale include base salary, equity, and benefits, with a salary range of $289,800-$362,250 USD for this full-time position in San Francisco, New York, and Seattle.

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Your work will directly evolve how we operate and measure our growth strategies, funnels, and pipelines. You will be responsible for building scalable data assets, adding contextual layers to data, and providing insights and conclusions to stakeholders.

To succeed in this role, you will need to be detail-oriented, rigorous about validating results, and talented at distilling down complexity. You will also need to love tackling and solving hard problems.

In addition to building scalable data assets, you will also be responsible for partnering with Data Engineers, Data Scientists, and cross-functional stakeholders to develop business metrics to understand our current performance and influence our roadmaps.

If you have 3+ years of industry experience in a highly analytical role, a degree in a quantitative field, and expert-level proficiency in writing complex SQL queries, we encourage you to apply.

As a Senior Data Analyst at Scale, you will have the opportunity to work with a talented team of professionals and contribute to the development of reliable AI systems for the world's most important decisions.

The ideal candidate will have excellent communication and presentation skills to executives, expertise at defining the right metrics, and diagnosing and understanding data inconsistencies.

Compensation packages at Scale 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.

You'll also receive benefits including comprehensive health, dental, and vision coverage, retirement benefits, a learning and development stipend, and generous PTO.

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Key responsibilities include:

• Owning the creation of model evaluation from initial hypothesis, data scraping to final publication.
• Deeply analysing why the model failed and identifying semantic patterns, edge cases, and systemic hallucinations in raw model outputs.
• Reviewing raw data sets, meeting transcripts, and research notes to identify the 'so what' and turning these findings into a logical hierarchy.
• Acting as the bridge between the data and the narrative by structuring findings into a logical hierarchy where the most critical 'hook' lands first, followed by the supporting evidence.

As a successful candidate, you will have 5-10 years of experience in DS, ML, AI research and analysis, be a structured thinker, have a high tolerance for ambiguity, and possess executive presence. Experience in Model Evaluation, ML Engineering or Technical Research is a plus.

This role offers a competitive compensation package, including base salary, equity, and benefits. The base salary range for this full-time position in San Francisco is $205,600-$257,000 USD.

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Your responsibilities will include:

Architecting and owning the ML foundations for commerce discovery: user, item, and interaction embeddings that power personalized recommendations across shop surfaces (homepage, cart, post-purchase, wishlist, and more).

Designing and deploying scalable real-time recommendation and ranking systems that support a growing catalog of 1P and 3P items across heterogeneous game publisher inventories.

Building ML-powered marketing targeting systems that identify the right users for the right campaigns , new buyer discounts, drop campaigns, weekly deals, and seasonal promotions , driving conversion without conditioning users to wait for discounts.

Leveraging Discord's unique social graph to build social commerce ML: gifting recipient prediction, group buying conversion modeling, and friend-group recommendations that differentiate Discord from traditional game storefronts.

Driving deep learning A/B testing infrastructure and model monitoring to translate experimentation results into actionable product decisions.

Partnering closely with Shop, Game Commerce, Revenue Infra, ML Infra, and Data Engineering teams to define ML requirements, surface integration points, and influence the commerce roadmap.

To be successful in this role, you will need:

4+ years of experience as a Machine Learning Engineer, with a track record of owning and shipping recommendation or personalization systems end-to-end.

Deep expertise in applied deep learning , particularly embedding models, two-tower architectures, and retrieval/ranking systems for e-commerce or content recommendation.

Strong proficiency in Python and deep learning frameworks (PyTorch preferred).

Experience building and operating real-time ML serving infrastructure at scale, including feature stores, model serving, and A/B testing frameworks.

Demonstrated ability to work in early-stage, high-ambiguity environments and build ML systems from the ground up, not just improve existing ones.

Experience translating ML evaluation metrics and experiment results into product roadmap decisions and business impact.

Strong cross-functional instincts , you're comfortable partnering with product, engineering, data science, and business stakeholders to align on priorities and drive execution.

Bonus skills include experience applying graph ML or social network signals (social affinities, community behavior) to recommendation or personalization problems, familiarity with personalized marketing systems: lifecycle targeting, audience segmentation, and campaign optimization, and familiarity with loyalty, rewards, or incentive programs.

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Your responsibilities will include identifying areas where ML can create value for our customers, identifying the right ML framing of product problems, working with teammates and Product and Design stakeholders, conducting exploratory data analysis and research, deeply understanding the problem area, researching and identifying the right algorithms and tools, being pragmatic, but innovating right to the cutting-edge when needed, performing offline evaluation to gather evidence an algorithm will work, working with engineers to bring prototypes to production, planning, measuring & socializing learnings to inform iteration, and partnering deeply with the rest of team, and others, to build excellent ML products.

To be successful in this role, you will need to have broad applied machine learning knowledge, 3-5 years applied ML experience, practical stats knowledge (experiment design, dealing with confounding etc), intermediate programming skills, strong communication skills, both within engineering teams and across disciplines, comfort with ambiguity, typically have advanced education in ML or related field (e.g. MSc), and scientific thinking skills. Bonus skills and attributes include track record shipping ML products, PhD or other experience in a research environment, deep experience in an applicable ML area. e.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering, strong stats or math background, visualization, data skills, SQL, matplotlib, etc.

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Key responsibilities include designing, implementing, and optimizing distributed training systems, developing high-performance optimizations, and establishing standards for reliability, maintainability, and security. You'll collaborate with researchers and engineers to build scalable infrastructure and publish learnings through internal documentation, open-source libraries, or technical reports.

We're looking for someone who blends deep systems and performance expertise with a curiosity for machine learning at scale. A strong understanding of deep learning frameworks, such as PyTorch, and experience working on distributed training for large models are preferred. If you have a track record of improving research productivity through infrastructure design or process improvements, that's a plus.

This role is based in San Francisco, California, and offers a competitive salary range of $350,000 - $475,000 USD per year, depending on background, skills, and experience. We sponsor visas and offer generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

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Key responsibilities include:

• Training state-of-the-art models, developed both internally and from the community, to deploy to our enterprise customers.
• Researching cutting-edge algorithms to integrate directly into our training stack.
• Building agents that leverage our proprietary agent-building algorithms to automatically hill climb datasets – including defining highly performant tools, multi-agent systems, and complex rewards.

Ideal candidates will have 1-3 years of building with LLMs in a production environment, experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO, and publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years.

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

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

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Key Responsibilities:

• Build cutting-edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
• Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas
• Use data-driven methods and leverage the unique properties of our data to improve candidate retrieval
• Work in a high-impact environment with quick experimentation and product launches
• Keep up with industry trends in recommendation systems

Requirements:

• 2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)
• End-to-end hands-on experience with building data processing pipelines, large-scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
• Degree in computer science, machine learning, statistics, or related field

Nice to Have:

• M.S. or PhD in Machine Learning or related areas
• Publications at top ML conferences
• Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring
• Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration
• Expertise in scalable real-time systems that process stream data
• Passion for applied ML and the Pinterest product

Relocation Statement:

This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

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

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

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

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

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

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

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Key responsibilities include:

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

Required qualifications include:

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

Preferred qualifications include:

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

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This role sits at the intersection of research and large-scale systems engineering: a builder who understands both the algorithms behind RL and the realities of distributed training and inference at scale. You'll wear many hats, from optimising rollout and reward pipelines to enhancing reliability, observability, and orchestration, collaborating closely with researchers and infra teams to make reinforcement learning stable, fast, and production-ready.

Responsibilities:

• Design, build, and optimise the infrastructure that powers large-scale reinforcement learning and post-training workloads.

• Improve the reliability and scalability of RL training pipeline, distributed RL workloads, and training throughput.

• Develop shared monitoring and observability tools to ensure high uptime, debuggability, and reproducibility for RL systems.

• Collaborate with researchers to translate algorithmic ideas into production-grade training pipelines.

• Build evaluation and benchmarking infrastructure that measures model progress on helpfulness, safety, and factuality.

• Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure.

We're looking for someone with strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases. You should have a good understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.

Experience training or supporting large-scale language models with tens of billions of parameters or more is a plus. Familiarity with monitoring and observability tools (Prometheus, Grafana, OpenTelemetry) is also a plus.

Logistics:

• Location: This role is based in San Francisco, California.

• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.

• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.

• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

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

Key responsibilities include:

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

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

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

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

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

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To accomplish this, you will be available to accommodate working with team members across different time zones, with one or two meetings per week starting at 8am PT or finishing at 7pm CET. You will also be ready to travel several times a year for company retreats and business events.

We value the benefits of in-person collaboration and prefer candidates who can easily and regularly connect at one of our office locations.

As a member of our team, you will receive a competitive compensation package, including $105K – $240K + Bonus + Equity. You will also enjoy 30 days paid vacation per year, comprehensive health insurance for US-based beginners, and a 401K with company match for US-based beginners and Direktversicherung for German beginners.

Additionally, you will participate in quarterly company-wide retreats, monthly wellness benefits, and budget for multiple visits per year to our offices in Berlin, Palo Alto, or Switzerland. You will also have access to the Learning & Development platform EdX and Hone, as well as a buddy to help you get settled.

Our team brings together vast expertise in molecular biology, machine learning, and software engineering, and we are all working towards becoming interdisciplinary, meaning we deepen the knowledge we have in our area of expertise while also expanding our knowledge of completely new fields.

We approach our goals with a beginner's mind, humbly and with fresh eyes, and aim to become the pioneers of a new discipline rooted in biology as much as in deep learning, whose impact will be realized together with out-of-the-box thinkers in business and entrepreneurship, defying established categorizations.

We are building a company culture centered around growth, learning, and discovery. We believe in humility and open-mindedness in how we approach each other, as well as problems we don't yet have solutions for.

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This role is ideal for someone who thrives at the intersection of research and systems engineering: a builder who understands both the math of optimization and the realities of distributed compute.

Responsibilities:

• Design and optimize distributed training infrastructure for large-scale LLMs, focusing on performance, stability, and reproducibility across multi-GPU and multi-node setups.
• Implement and evaluate low-precision numerics (for example, BF16, MXFP8, NVFP4) to improve efficiency without sacrificing model quality.
• Develop kernels and communication primitives that use hardware-level support for mixed and low-precision arithmetic.
• Collaborate with research teams to co-design model architectures and training recipes that align with emerging numeric formats and stability constraints.
• Prototype and benchmark scaling strategies such as data, tensor, and pipeline parallelism that integrate precision-adaptive computation and quantized communication.
• Contribute to the design of our internal orchestration and monitoring systems to ensure that thousands of distributed experiments can run efficiently and reproducibly.
• Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure.

Skills and Qualifications:

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
• Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.
• Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases in areas such as floating-point numerics, low-precision arithmetic, and distributed systems.

Preferred qualifications , we encourage you to apply if you meet some but not all of these:

• Familiarity with distributed frameworks such as PyTorch/XLA, DeepSpeed, Megatron-LM.
• Experience implementing FP8, INT8, or block-floating point (MX) formats and understanding their numerical trade-offs.
• Prior contributions to open-source deep learning infrastructure such as PyTorch, DeepSpeed, or XLA.
• Publications, patents, or projects related to numerical optimization, communication-efficient training, or systems for large models.
• Experience training and supporting large-scale AI models.
• Track record of improving research productivity through infrastructure design or process improvements.

Logistics:

• Location: This role is based in San Francisco, California.
• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

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We're looking for a Senior Machine Learning Engineer to join our Monetization team. As a key member of the team, you will be responsible for developing and executing a vision for the evolution of the machine learning technology stack within Ads.

Key responsibilities include:

• Building cutting-edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
• Partnering closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search)
• Using data-driven methods and leveraging the unique properties of our data to improve candidate retrieval
• Working in a high-impact environment with quick experimentation and product launches
• Keeping up with industry trends in recommendation systems

Requirements:

• 2+ years of industry experience applying machine learning methods
• Degree in computer science, statistics, or related field; or equivalent experience
• End-to-end hands-on experience with building data processing pipelines, large-scale machine learning systems, and big data technologies
• Practical knowledge of large-scale recommender systems, or modern ads ranking, retrieval, targeting, marketplace systems

Nice to have:

• M.S. or PhD in Machine Learning or related areas
• Publications at top ML conferences
• Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring
• Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration
• Expertise in scalable real-time systems that process stream data
• Passion for applied ML and the Pinterest product
• Background in computational advertising

Relocation Statement:

This position is not eligible for relocation assistance.

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Key responsibilities include:

• Drive strategy that fields safe and secure performant agents on embedded warfighting compute.
• Manage a cohort of cleared machine learning engineers, researchers, and product managers.
• Provide deep technical leadership to team on design, development, and deployment of advanced AI solutions.
• Leverage and shape the world's largest defense robotics data set to develop cutting-edge AI capabilities.
• Coordinate with internal testing and evaluation team(s) to design specialized evaluation benchmarks for defense use-cases.
• Partner with US-based Frontier AI Labs to revolutionize the employment of autonomy in embedded hardware.

Required qualifications include:

• Management experience leading and growing AI/ML teams.
• Experience deploying AI-technologies into classified environments (e.g., disconnected, air-gapped, etc.).
• Expertise in deep learning techniques and the latest generative AI technologies (Agents, Vision-Language Action models, etc.).
• Strong programming skills.
• Eligible to obtain and maintain an active U.S. Top Secret SCI security clearance.

Preferred qualifications include:

• Experience with edge-deployed ML systems.
• Prior work in Defense Tech and/or Start Ups.
• Active U.S. Top Secret SCI security clearance.

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

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

Your Impact

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

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

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

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

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

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

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

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

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

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

What You Bring

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

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

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

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

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

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

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

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

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

Labelbox Applied Research

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

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

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

The Role

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

Key responsibilities:

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

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

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

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

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

About You

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

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

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

• Deep understanding of statistics is strongly preferred

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

• Strong knowledge of systems design and data structures

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

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

• A passion for Artificial Intelligence.

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

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We're looking for someone with 5-8 years of applied ML experience, previous background in a senior/staff role, and significant demonstrated impact that their work has had on the product and/or the teams. You should have strong programming skills, experience as the primary technical leader for a team, and strong communication skills, both within engineering teams and across disciplines.

As a bonus, we're looking for someone with a track record of shipping ML products, PhD or other experience in a research environment, deep experience in an applicable ML area, and strong stats or math background.

We offer competitive salary and equity in a fast-growing start-up, regular compensation reviews, pension scheme, life assurance, comprehensive health and dental insurance, flexible paid time off policy, paid maternity leave, and relocation support for those moving to our offices.

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This role is perfect for an engineer who enjoys working close to the metal and across the research boundary. You'll collaborate with researchers and systems architects to bridge algorithmic design with hardware efficiency. You'll prototype new kernel implementations, profile performance across hardware generations, and help define the numerical and parallelism strategies that determine how we scale next-generation AI systems.

Responsibilities

• Design and implement custom ML kernels (e.g., CUDA, CuTe, Triton) for core LLM operations such as attention, matrix multiplication, gating, and normalization, optimized for modern GPU and accelerator architectures.
• Design and think through compute primitives to reduce memory bandwidth bottlenecks and improve kernel compute efficiency.
• Collaborate with research teams to align kernel-level optimizations with model architecture and algorithmic goals.
• Develop and maintain a library of reusable kernels and performance benchmarks that serve as the foundation for internal model training.
• Contribute to infrastructure stability and scalability, ensuring reproducibility, consistency across precision formats, and high utilization of compute resources.
• Document and share insights through internal talks, technical papers, or open-source contributions to strengthen the broader ML systems community.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
• Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases
• Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.
• Proficiency in CUDA, CuTe, Triton, or other GPU programming frameworks.
• Demonstrated ability to analyze, profile, and optimize compute-intensive workloads.

Preferred qualifications:

• Experience training or supporting large-scale language models with tens of billions of parameters or more.
• Track record of improving research productivity through infrastructure design or process improvements.
• Experience developing or tuning kernels for deep learning frameworks such as PyTorch, JAX, or custom accelerators.
• Familiarity with tensor parallelism, pipeline parallelism, or distributed data processing frameworks.
• Experience implementing low-precision formats (FP8, INT8, block floating point) or contributing to related compiler stacks (e.g., XLA, TVM).
• Contributions to open-source GPU, ML systems, or compiler optimization projects.
• Prior research or engineering experience in numerical optimization, communication-efficient training, or scalable AI infrastructure.

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Your measure of success is the number of workflows you've permanently transformed and the extent to which those in your group start any task with an AI tool.

Responsibilities:

• Identify and document the highest-leverage workflow transformations within your group's day-to-day work by deeply understanding their processes and outputs
• Build custom tools, agents, automations, and skills tailored to each marketer's specific responsibilities
• Coach and partner with each marketer through a progressive journey: from awareness, to first win, to regular AI integration, to full workflow transformation, to self-sufficiency
• Teach marketers to build and iterate on their own tools over time, creating independence
• Recognize patterns across your cohort and systematically scale what works , a tool built for one marketer should become reusable for their peers
• Document every tool, playbook, and transformation pattern you build so the entire FDA team can use each other's work
• Share wins visibly within your cohort and with leadership to build momentum, inspire, and celebrate success
• Track individual and cohort progress rigorously against a defined maturity model, moving every marketer toward self-sustaining, AI-first work
• Prepare marketers for an agentic future , not just prompt writing, but designing, building, and overseeing autonomous, multi-agent workflows

Who you are:

We're looking for people who have already lived the transformation they'll be driving for others. You've used AI to fundamentally change how you work , not as a novelty, but as your default operating mode.

• You are a deep AI practitioner. You build agents, automations, and tools fluently. You don't just know what AI can do in theory , you've built things that changed how real work gets done. You can build in real time alongside the people you support.
• You are an exceptional coach and communicator. You can meet people wherever they are. You create desire for progress, and you adapt your approach to each person.
• You understand marketing work. You either have direct experience in marketing or can quickly learn the workflows, deliverables, tools, and pressures of your assigned group. You know that understanding someone's work is a prerequisite to transforming it.
• You are a pattern recognizer. When you build something that works for one person, you immediately see how it applies to others. You think in systems, not one-off solutions.
• You are high-caliber, persistent, and versatile. Your skills and experience are valuable across the company. You're the kind of person who could succeed in many roles at Stripe.
• You bias toward action and speed. You'd rather show someone a working proof-of-concept on their own deliverable today than present a polished deck about what's theoretically possible next quarter.
• You are comfortable with ambiguity and evolution. This is a new team building a new operating model. The playbook will be rewritten as we learn. You thrive in that environment.

Minimum requirements:

• 5+ years of professional experience in a role requiring analytical thinking, problem-solving, and cross-functional collaboration
• Demonstrated, hands-on experience building AI-powered tools, agents, automations, or workflows that transformed real work processes (not just using AI as a chatbot , building with it)
• Track record of coaching, teaching, or enabling others , formally or informally , with evidence that people you've helped actually changed how they work
• Strong written and verbal communication skills, with the ability to explain technical concepts to non-technical audiences and adapt your style to different learners
• Comfort working across multiple workstreams and relationships simultaneously (you'll be supporting ~20 marketers at varying stages of their AI journey)

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Responsibilities:

• Lead and execute complex data science projects that directly advance our drug development portfolio
• Develop and implement sophisticated models for therapeutic hypothesis evaluation, including patient stratification and biomarker identification
• Design and create AI models for modernizing clinical trial evaluations, including surrogate endpoints
• Aid in the development and training of AI agents to automate and optimize biomedical workflows
• Collaborate cross-functionally with clinical, technical, and research teams
• Present complex analytical findings to senior stakeholders, including executive leadership

About You:

• Required Qualifications:

+ PhD in computational sciences or life sciences + 3+ years of post-academic experience in life sciences (biotech, pharma, consulting) + Strong programming skills, particularly in Python + Extensive experience in multi-modal bioinformatics analysis

• Preferred Qualifications:

+ Proven expertise in cloud computing environments, including proficiency with tabular and/or graph databases + Strong background in machine learning and deep learning, particularly in biological applications + Experience with large language models (LLM) + Demonstrated ability to collaborate effectively with engineering teams on production systems + Strong communication skills with proven ability to present complex technical findings to senior stakeholders

Total Compensation Range: $170,000 - $215,000

Where We Hire:

Formation Bio is prioritizing hiring in key hubs, primarily the New York City and Boston metro areas, with a hybrid model requiring 3 days per week in office. Applicants from the Research Triangle (NC) and San Francisco Bay Area may also be considered.

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Your mission will be to embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise. You will identify and source new business opportunities with biotech and pharma through market research, networking, and by building business relationships to expand Inceptive’s network.

Key responsibilities include leading outbound BD efforts, including prospecting, relationship building, and pipeline management, as well as supporting deal execution (term sheets, negotiations, diligence, closing) and collaborating with scientific and technical teams to translate platform capabilities into partner value.

To succeed in this role, you will need a Master's in science (PhD preferred), ideally with a background in biologics, genetic medicines, or deep learning methods applied to drug development, and 3 years of experience in business development in pharma, biotech or VC.

The salary range for this position is $135K – $240K + Bonus + Equity.

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

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

Responsibilities:

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

Skills and Qualifications:

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

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

Logistics:

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

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

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

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

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

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

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

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

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

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

What You Bring

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

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

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

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

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

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

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

Strong analytical and problem-solving abilities in ambiguous situations.

Excellent communication skills.

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

Labelbox Applied Research

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

Life at Labelbox

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

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

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

Growth: Career advancement opportunities directly tied to your impact

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

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The design space of biomolecules is unimaginably vast , far beyond what can be explored experimentally. Yet within this space lie molecules with properties essential for new medicines. Our machine learning models learn to design therapeutic biomolecules with specific, desirable functions.

We advance the state of the art in molecular design by training large-scale foundation models and developing cutting-edge generative approaches. The models learn from diverse heterogeneous datasets and are refined through focused fine-tuning and feedback from experiments. Key to progress is a team that combines exceptional machine learning expertise with thorough domain understanding.

You will collaborate closely with other machine learning researchers and engineers, as well as computational and experimental biologists, to advance these models and translate their capabilities into real therapeutic designs.

Responsibilities

• Embody our vision of an interdisciplinary environment and embrace learning about areas outside of your traditional area of expertise

• Develop, implement, train, and iteratively improve state-of-the-art models for biomolecule design

• Analyze, visualize, and communicate results to support team efforts in improving models and data

• Create, deploy, and refine tools for efficient, reliable machine learning experimentation and production

• Work with biologists to collect data for the training and evaluation of generative models of biomolecules

• Provide mentorship and technical direction to team members as appropriate

Qualifications

• 3+ years of hands-on experience developing ML models

• Demonstrated track record of implementing, training, improving advanced machine learning models

• Highly capable programmer fluent in Python ecosystem and PyTorch or similar deep learning framework

• Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET

• Readiness to travel several times a year for company retreats and business events

Compensation

$200K – $275K + Bonus + Equity

Benefits

• A competitive compensation package

• 30 days paid vacation per year

• Comprehensive health insurance for US based employees

• 401K with company match for US based employees and Direktversicherung for German employees

• Quarterly company-wide retreats

• Monthly wellness benefit

• Budget for multiple visits per year to our offices in Berlin, Palo Alto or Switzerland

• Learning & Development budget to attend conferences, take courses, or otherwise invest in your professional growth, as well as access to the Learning & Development platform EdX and Hone

• A buddy to help you get settled

At Inceptive, we are creating tools to develop increasingly powerful biological software for the rational design of novel, broadly accessible medicines and biotechnologies previously out of reach. Our team brings together vast expertise in molecular biology, machine learning, and software engineering, and we are all working towards becoming interdisciplinary, meaning we deepen the knowledge we have in our area of expertise while also expanding our knowledge of completely new fields.

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You will work on real-world projects, collaborate with experienced professionals, gain valuable experience in the fintech industry, and realise business and social impact. This role requires hybrid work from our Mountain View office, with 2 days a week in person. This internship will pay $40 per hour, with an expected 40 hours per week for the 12-week program.

Responsibilities:

• Train and fine-tune large-scale Foundation Models to support various fintech product use cases
• Work with a large dataset, including structured and unstructured data
• Help in ensuring improvements in our current ML systems via model, data, or experimentation upgrades
• Gain hands-on experience with a wide array of technologies, including PyTorch, AWS, Kafka, Databricks, etc

Requirements:

• Actively pursuing a Master's or PhD in Computer Science, Information Technology, or a related field
• Located in Mountain View, or have the ability to relocate there, for the duration of the internship
• Strong understanding of statistical models, familiarity, and in-depth understanding of machine learning and deep learning algorithms. Familiarity with training or fine-tuning large-scale models, Sequence Transformer models
• Interest in multimodal or multitask learning across structured, sequential, and behavioural data
• Familiarity with AI tools, harness engineering, agentic workflow, etc.
• Hands-on programming experience in Python and ML frameworks such as PyTorch
• Equipped with good verbal and written communication skills
• A background demonstrating strong problem-solving skills
• Committed to taking ownership of projects, conducting thorough investigations, and driving initiatives to conclusion

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As a machine learning engineer on our team, you'll develop advanced ML solutions that directly impact Stripe's payment products and core business metrics.

About the team

The Payments ML Accelerator team is developing foundational ML capabilities that drive innovation across Stripe's payment products. We build deep learning models that tackle Stripe's most complex payment challenges - from fraud detection to authorization optimization - and deliver measurable business impact.

Responsibilities

• Design and deploy deep learning architectures and foundation models to address problems across key payment entities such as merchants, issuers, or customers
• Identify high-impact opportunities, and drive the long-term ML roadmap through well-scoped high-leverage initiatives
• Architect generalizable ML workflows to enable rapid scaling and optimized online performance
• Deploy ML models online and ensure operational stability
• Experiment with advanced ML solutions in the industry and ideate on product applications
• Explore cutting-edge ML techniques and evaluate their potential to solve business problems
• Work closely with ML infrastructure teams to shape new platform capabilities

Who you are

We are looking for ML Engineers who are passionate about using ML to improve products and delight customers. You have experience developing streaming feature pipelines, building ML models, and deploying them to production, even if it involves making substantial changes to backend code. You are comfortable with ambiguity, love to take initiative, and have a bias towards action.

Minimum requirements

• Minimum 7 years of industry experience doing end-to-end ML development on a machine learning team and bringing ML models to production
• Proficient in Python, Scala, and Spark
• Proficient in deep learning and LLM/foundation models

Preferred qualifications

• MS/PhD degree in quantitative field or ML/AI (e.g. computer science, math, physics, statistics)
• Knowledge about how to manipulate data to perform analysis, including querying data, defining metrics, or slicing and dicing data to evaluate a hypothesis
• Experience evaluating niche and upcoming ML solutions

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We work closely with some of the most advanced AI teams in the world, helping them build, optimize, and scale their ML and GenAI workflows across industries such as computer vision, robotics, natural language processing, and large language models (LLMs).

We’re hiring an AI Solutions Engineer, Post-Sales to help customers solve real-world problems by enabling them to implement and scale ML pipelines and agentic workflows using Weights & Biases. In this role, you’ll collaborate with engineering teams to ensure smooth onboarding and adoption, act as a trusted advisor on best practices, and represent the voice of the customer internally.

You will partner directly with leading AI teams to optimize workflows, share technical expertise, and influence our product roadmap based on real-world customer feedback.

This is an ideal opportunity for ML practitioners who are customer-focused and eager to work with top AI companies globally.

Responsibilities

• Collaborate with engineering teams to ensure smooth onboarding and adoption of Weights & Biases
• Act as a trusted advisor on best practices for implementing and scaling ML pipelines and agentic workflows
• Represent the voice of the customer internally and influence our product roadmap based on real-world customer feedback
• Partner directly with leading AI teams to optimize workflows and share technical expertise

Requirements

• 3–5 years of relevant experience in a similar role
• Strong programming proficiency in Python
• Hands-on experience enabling production-grade ML systems, with a focus on training and inference pipelines, experiment tracking, deployment patterns, and observability using deep learning frameworks (TensorFlow/Keras, PyTorch/PyTorch Lightning) and MLOps tooling (e.g. Airflow, Kubeflow, Ray, TensorRT)
• Familiarity with cloud platforms (AWS, GCP, Azure)
• Experience with GenAI/LLMs and related tools (e.g. LangChain/LangGraph, HuggingFace Transformers, Pinecone, Weaviate)
• Strong experience with Linux/Unix
• Excellent communication and presentation skills, both written and verbal
• Ability to break down and solve complex problems through customer consultation and execution

Preferred

• Background in robotics
• TypeScript experience
• Proficiency with Fastai, scikit-learn, XGBoost, or LightGBM
• Background in data engineering, MLOps, or LLMOps, with tools such as Docker and Kubernetes
• Familiarity with data pipeline tools

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We believe skill with AI is fundamental to human agency. Education Labs sits inside Product Research, which means we build learning products with a front-row seat to what's coming. We're close to the people shaping Claude's next capabilities, and our job is to make sure the world can actually use them.

Responsibilities

Build at the frontier - Co-develop vision and own strategy and execution for AI-native learning products , the adaptive systems, assessments, and in-product experiences that will define how people learn with and about frontier AI Work closely with research teams to understand emerging capabilities and what they unlock for learners , you're translating research into product, not waiting for a spec Prototype and experiment to validate ideas quickly, using Claude and our internal tools as building blocks Anticipate how capability shifts change the product and build with that trajectory in mind Stay close to learners , run user research, watch real people move from "I don't understand this" to "I could teach this," and let what you observe reshape what we build

Be a thought partner Push the team's thinking on what education should look like as AI capabilities accelerate , help us spot what we're missing, not just execute what we've already decided Bring product rigor to a team that's scaling fast: clear priorities, sharp tradeoffs, honest metrics that connect learning to real behavior change Act as a bridge between research, product, GTM, marketing, comms, and the education team, translating in all directions

See the whole picture Collaboratively define success metrics grounded in demonstrated understanding, skill progression, and lasting agency , not time-on-site or completion counts Keep a hand in the success of adjacent team efforts, helping the overall education portfolio work as a system rather than a collection of projects Partner across Anthropic , product teams, research, GTM, societal impacts, marketing , to keep education woven into how we ship and scale Run the program, not just the product: drive stakeholder alignment across technical and non-technical partners, keep workstreams on track, and own the operational rhythm that turns strategy into shipped work

You may be a good fit if you have

5+ years in product management, with a track record of shipping products from zero to one and seeing them through to real impact Technical fluency , you're comfortable with AI tools, can prototype with Claude, and hold your own in conversations with researchers and engineers Genuine curiosity about frontier AI , you follow what's emerging, you play with new capabilities, and you have opinions about where things are heading Comfort with ambiguity and wide scope , you make good calls with incomplete information and you're not precious about where your job ends A track record as a force multiplier on small teams , you make the people around you more effective, not just your own roadmap Strong written and verbal communication , you can write a crisp spec, present to leadership, and synthesize messy stakeholder input into a clear direction Conviction that education should build agency, not dependency , you want to teach people to think with AI, and you believe that matters

Strong candidates may also have

Experience working closely with research or applied science teams, or a background that makes you fluent in how research becomes product Background in learning products, developer education, or curriculum design Founder experience or time at an early-stage company where scope was wide and you wore many hats Familiarity with how people actually learn , learning science, instructional design, or strong instincts from having taught something yourself Experience building with LLMs as core product infrastructure, not just as a feature

What this role is not

This is a hands-on IC product, project, and program management role. You'll shape strategy, push the team's thinking, and have real influence over what education at Anthropic becomes, and coordinate complex execution across multiple stakeholders and teams , but it doesn't involve people management. If you're looking to immediately lead a team, this isn't the right fit. If you want broad scope and high autonomy as a builder, it might be.

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The ideal candidate will have 7+ years of experience with a BSEE or related field, strong experience with DSP implementation for embedded devices and/or software defined radios, strong knowledge of Python and MATLAB, experience with CUDA or GPU accelerated frameworks like cuSignal, experience with embedded devices, including FPGA, Nvidia Jetson, and Software Defined Radios, skilled with Modeling and Simulation of RF systems including Radar and SAR, familiar with deep learning algorithms, experience with ML frameworks such as TensorFlow and PyTorch, familiar with wireless communication standards (Bluetooth, 3G/4G/5G, Wi-Fi, SINCGARS, MUOS, etc.), excellent at balancing multiple projects at any given time and/or managing a larger team for a larger program, enthusiastic about both working with a team and executing some work individually (depending on program scope), experience with Electronic Warfare systems, and currently possesses and is able to maintain an active U.S. Secret security clearance.

Preferred qualifications include a Master's or PhD degree in Electrical, Electronics, Computer Engineering, or related fields, defense, national security, or aerospace domain familiarity through industry or education, extensive Digital Signal Processing (DSP) knowledge and experience, expertise in Synthetic Aperture Radar (SAR) and/or Inverse SAR (ISAR): Image formation, waveforms, phenomenology, modeling and simulation.

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What you'll do at Figma:

• Drive fundamental and applied research in AI.
• Explore the boundaries of what is possible with the current technology set to build best in class models for Figma's domains.
• Combine industry best practices and a first-principles approach to build cutting edge Generative AI models, using techniques like Supervised Finetuning (SFT), Reinforcement Learning (RL), prompt improvements and synthetic data generation.
• Work in concert with product and infrastructure engineers to improve Figma's products through AI powered features.
• Collaborate closely with product managers and engineers to transform user feedback into requirements for AI systems.
• Build evaluation systems to measure and improve quality of AI features in Figma products.

We'd love to hear from you if you have:

• Extensive experience in building generative AI features through prompt engineering, and fine tuning models in production environments.
• Experience working on deep learning and generative AI frameworks like PyTorch, JAX, HuggingFace etc.
• Experience training LLMs with Reinforcement Learning techniques such as preference-based RL (DPO, PPO) and/or RL with verifiable rewards (RLVR) such as GRPO/DAPO.
• 4+ years in Generative AI, and 6+ years of experience in one or more of the following areas: machine learning, natural language processing/understanding, computer vision.
• Strong software engineering skills with 5+ years of experience in programming languages (Python, C++, Java or R).
• Experience communicating and working across functions to drive solutions.

While not required, It’s an added plus if you also have:

• Proven track record of planning multi-year roadmap in which shorter-term projects ladder to the long-term vision.
• Experience in mentoring/influencing senior engineers across organizations.
• Expertise working on large scale and distributed AI training.

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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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This Research Scientist will focus on scoping, evaluating, red teaming, and defending against societal risks caused by advanced models that emerge over the next few years.

What You’ll Do:

• Design and run research experiments to understand the emerging risks models may create
• Produce internal & external artifacts (research, products, demos, dashboards, tools) that communicate the state of model capabilities
• Shape product, safeguards, and training decisions based on what you find
• Work closely with Societal Impacts (SI) and Safeguards teams

Sample Projects:

• Build, run, and study an autonomous AI-powered business (e.g. Project Vend), then identify the growth of real autonomous businesses in the wild using Clio and other tools
• Build a benchmark for a model’s national security capabilities
• Red team unsafeguarded models’ abilities to be used for control
• Identify indicators of models being used to scale movements that rely on social control

You May Be a Good Fit If You:

• Are a fast experimentalist who ships research quickly
• Have experience creating a research program from scratch
• Are thoughtful about humanity’s adaptation to powerful AI systems in our economy and society
• Can communicate thoughtfully in written + spoken form with a wide range of stakeholders
• Can scope ambiguous research questions into tractable first projects

Strong candidates may also have experience with:

• Building & maintaining large, foundational infrastructure
• Building simple interfaces that allow non-technical collaborators to evaluate AI systems
• Working with and prioritizing requests from a wide variety of stakeholders, including research and product teams

Annual Compensation Range:

$320,000-$850,000 USD

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Key responsibilities include designing, running, and analyzing scientific experiments to advance our understanding of large language models. Additionally, you will optimize and scale our training infrastructure to improve efficiency and reliability, and develop and improve dev tooling to enhance team productivity.

As a Research Engineer, you will contribute to the entire stack, from low-level optimizations to high-level model design. You will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.

The ideal candidate will have an advanced degree in Computer Science, Machine Learning, or a related field, and strong software engineering skills with a proven track record of building complex systems. You should be familiar with Python and experience with deep learning frameworks, particularly PyTorch. Additionally, you should have expertise in large-scale machine learning, particularly in the context of language models.

You will thrive in this role if you have significant software engineering experience, are results-oriented with a bias towards flexibility and impact, willing to take on tasks outside your job description to support the team, enjoy pair programming and collaborative work, and are eager to learn more about machine learning research.

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This is a high-impact team driving DAU growth and long-term user retention by connecting users to what matters most to them. If applying ML / AI in production to improve the relevance of Reddit Notifications excites you, then you’ve found the right place.

Responsibilities:

• Lead the team that architects and designs notifications relevance at Reddit.
• Guide team on holistic, adaptive systems covering budgeting optimization, candidate retrieval, and ranking.
• Work with ML engineers to design, implement, and optimize machine-learning models that drive personalization and user re-engagement.
• Participate in the full development cycle: design, develop, QA, experiment, analyze, and deploy.
• Build and maintain a diverse team that can collaborate across disciplines to find technical solutions to complex challenges.
• Serve as a thought partner to product and upper management to ensure your team’s plans align with company goals.
• Communicate your team’s work and set expectations with cross-functional stakeholders.
• Help your engineers identify career goals and create development plans to achieve them.
• Constantly seek opportunities to push your engineers & managers outside their comfort zone and turn followers into leaders.

Requirements:

• 2+ years of experience building and managing engineering teams.
• 5+ years of experience as a Machine Learning Engineer or Software Engineer working on large-scale machine learning systems.
• Deep understanding of building and deploying large-scale recommender systems (retrieval + ranking) in production.
• Hands-on experience working with deep learning models, sequential features and real-time systems.
• Experience with distributed training and inference using tools like Ray, PyTorch Distributed, or similar.
• Familiarity with reinforcement learning or multi-objective optimization in recommendation systems.
• Entrepreneurial and self-directed, innovative, results-oriented, biased towards action in fast-paced environments.
• Able to communicate and discuss complex topics with technical and non-technical audiences.
• Able to tackle ambiguous and undefined problems.

Benefits:

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

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From statistical models all the way down to deep and foundational models, feature augmentation and auto-tuning, our Applied ML/AI team works on some of the most complex, most interesting problems facing businesses, making Databricks' infrastructure and products as performant and cost-efficient as possible.

This is a high-impact problem as our customers look at us to deliver the most out of their data.

Key responsibilities include:

• Building features and running end-to-end systems in a small team of experienced engineers and data scientists.
• Shaping the direction of our applied ML investment by engaging with engineering and product teams across the company.
• Driving the development and deployment of state-of-the-art ML/AI models and systems that directly impact the capabilities and performance of Databricks’ products, infrastructure, and services.
• Architecting and implementing robust, scalable ML infrastructure, including model training and serving components to support seamless integration of AI/ML models into production environments.
• Working on novel modeling techniques in the field of ML for forecasting.

What we look for:

• 2-8 years of machine learning engineering experience in high-velocity, high-growth companies.
• Strong understanding of both computer systems and statistics.
• Experience developing AI/ML systems at scale in production.
• Strong track record of ML modeling that goes beyond using standard libraries.
• Strong coding and software engineering skills, and familiarity with software engineering principles around testing, code reviews, and deployment.
• A large breadth of knowledge or willingness to develop mathematical modelling beyond the ML.

Why Join Us?

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

If you're excited about pushing the boundaries of AI in real-world applications, we’d love to hear from you!

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You'll work at the intersection of advanced AI development and real-world impact, enhancing the ability to connect users with relevant content, accounts, and experiences.

Responsibilities:

• Designing and architecting recommendation algorithms across various product surfaces

• Leveraging all of xAI's infrastructure and AI stacks to dramatically enhance the user experience

• Writing data pipelines and training jobs that continuously learn from product data

• Iterating and improving the algorithm by gathering user feedback in real time through experimentation

• Ensuring scalability and efficiency of machine learning systems

Basic Qualifications:

• Knowledge of data infrastructure like Kafka, Clickhouse, and Spark

• Experienced in implementing recommender systems and/or deep learning applications at industrial scale

• Skilled in one or more DL software frameworks such as JAX or PyTorch

• Exceptional candidates may be experienced in writing CUDA kernels

Compensation and Benefits:

$180,000 - $440,000 USD

Base salary is just one part of our total rewards package at xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

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In this dynamic role, you will design state-of-the-art algorithms and systems that enable safe, efficient, and intelligent autonomous capabilities.

Today, employing mass quantities of autonomous robots requires heavy human oversight and execution. Anduril is leveraging AI approaches to improve effectiveness of autonomous missions, offload operator burden, and speed up execution via realtime monitoring, recommendations to users, and multi-modal interaction patterns.

You will apply proven and un-proven approaches to create prototypes for expanding the capability of autonomous systems.

Responsibilities

• Develop Advanced Agentic Software
• Design and implement novel agent-based software systems to improve sensor perception, prediction, and decision-making for autonomous vehicles
• Apply Agentic Reasoning
• Design and implement integrated agents and AI models to solve for end-user autonomous systems workflows.
• End-to-End System Integration
• Collaborate with cross-functional teams to integrate research prototypes into robust, production-ready systems including simulation environments and real-world platforms.
• Research & Experimentation
• Conduct research into reinforcement learning strategies and deep architectures, iterate on experimental designs, and evaluate performance using rigorous quantitative metrics.
• Data-Driven Innovation
• Utilize real-world and synthetic data to enhance model robustness and generalization, leveraging scalable training pipelines on distributed systems.

Required Qualifications

• Sophisticated knowledge of LLM's with an understanding of how they work and how they're applied
• Solid experience with reinforcement learning methods and their application to autonomous systems.
• Proven experience of shipping products end to end
• Experience with simulation or real-world validation for autonomous vehicles is highly desirable.
• A degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience
• Eligible to obtain and maintain an active U.S. Top Secret security clearance

Preferred Qualifications

• PhD or Master’s degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience
• Novel application track record and experience including first author publications, participation in peer reviewed conferences, contribution to open source projects, and demonstrated contribution to the ML and AI community.
• Proven experience in deep learning research and development, particularly in generative AI. This includes diffusion models and autoregressive generative models.
• Experience in multi-modal sensor data processing (e.g., cameras, LiDAR, radar).
• Familiarity with ML Ops best practices, including model versioning and reproducible research pipelines.
• Strong programming skills in Python and familiarity with C/C++ is a plus.
• General software engineering experience solving motion planning or related robotics problems.

Salary and Benefits

The salary range for this role is $254,000-$336,000 USD. Highly competitive equity grants are included in the majority of full-time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including:

• Healthcare Benefits - US Roles: Comprehensive medical, dental, and vision plans at little to no cost to you.
• UK & AUS Roles: We cover full cost of medical insurance premiums for you and your dependents.
• IE Roles: We offer an annual contribution toward your private health insurance for you and your dependents.
• Income Protection: Anduril covers life and disability insurance for all employees.
• Generous time off: Highly competitive PTO plans with a holiday hiatus in December.
• Caregiver & Wellness Leave is available to care for family members, bond with a new baby, or address your own medical needs.
• Family Planning & Parenting Support: Coverage for fertility treatments (e.g., IVF, preservation), adoption, and gestational carriers, along with resources to support you and your partner from planning to parenting.
• Mental Health Resources: Access free mental health resources 24/7, including therapy and life coaching.
• Additional work-life services, such as legal and financial support, are also available.
• Professional Development: Annual reimbursement for professional development.
• Commuter Benefits: Company-funded commuter benefits based on your region.
• Relocation Assistance: Available depending on role eligibility.
• Retirement Savings Plan - US Roles: Traditional 401(k), Roth, and after-tax (mega backdoor Roth) options.
• UK & IE Roles: Pension plan with employer match.
• AUS Roles: Superannuation plan.

Protecting Yourself from Recruitment Scams

Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information.

To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind:

• No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates.
• Please always verify communications:

Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com. Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links.

What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com.

Your proactive approach in protecting yourself from recruitment scams is greatly appreciated.

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You'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with.

Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

We conduct all interviews in Python, and this role may require responding to incidents on short-notice, including on weekends.

Responsibilities:

Implement and optimize post-training techniques at scale on frontier models

Conduct research to develop and optimize post-training recipes that directly improve production model quality

Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation

Develop tools to measure and improve model performance across various dimensions

Collaborate with research teams to translate emerging techniques into production-ready implementations

Debug complex issues in training pipelines and model behavior

Help establish best practices for reliable, reproducible model post-training

You may be a good fit if you:

Thrive in controlled chaos and are energized, rather than overwhelmed, when juggling multiple urgent priorities

Adapt quickly to changing priorities

Maintain clarity when debugging complex, time-sensitive issues

Have strong software engineering skills with experience building complex ML systems

Are comfortable working with large-scale distributed systems and high-performance computing

Have experience with training, fine-tuning, or evaluating large language models

Can balance research exploration with engineering rigor and operational reliability

Are adept at analyzing and debugging model training processes

Enjoy collaborating across research and engineering disciplines

Can navigate ambiguity and make progress in fast-moving research environments

Strong candidates may also:

Have experience with LLMs

Have a keen interest in AI safety and responsible deployment

We welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems.

However, proficiency in Python, deep learning frameworks, and distributed computing is required for this role.

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

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You will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.

Responsibilities

• Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development
• Independently lead small research projects while collaborating with team members on larger initiatives
• Design, run, and analyze scientific experiments to advance our understanding of large language models
• Optimise and scale our training infrastructure to improve efficiency and reliability
• Develop and improve dev tooling to enhance team productivity
• Contribute to the entire stack, from low-level optimisations to high-level model design

Qualifications & Experience

• Degree (BA required, MS or PhD preferred) in Computer Science, Machine Learning, or a related field
• Strong software engineering skills with a proven track record of building complex systems
• Expertise in Python and deep learning frameworks
• Have worked on high-performance, large-scale ML systems, particularly in the context of language modelling
• Familiarity with ML Accelerators, Kubernetes, and large-scale data processing
• Strong problem-solving skills and a results-oriented mindset
• Excellent communication skills and ability to work in a collaborative environment

You'll thrive in this role if you

• Have significant software engineering experience
• Are able to balance research goals with practical engineering constraints
• Are happy to take on tasks outside your job description to support the team
• Enjoy pair programming and collaborative work
• Are eager to learn more about machine learning research
• Are enthusiastic to work at an organisation that functions as a single, cohesive team pursuing large-scale AI research projects
• Have ambitious goals for AI safety and general progress in the next few years, and you’re excited to create the best outcomes over the long-term

Sample Projects

• Optimising the throughput of novel attention mechanisms
• Proposing Transformer variants, and experimentally comparing their performance
• Preparing large-scale datasets for model consumption
• Scaling distributed training jobs to thousands of accelerators
• Designing fault tolerance strategies for training infrastructure
• Creating interactive visualisations of model internals, such as attention patterns

Benefits

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, flexible working hours, and a lovely office space in which to collaborate with colleagues.

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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.

About the role

Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with.

You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models.

Responsibilities

• Implement and optimize post-training techniques at scale on frontier models
• Conduct research to develop and optimize post-training recipes that directly improve production model quality
• Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation
• Develop tools to measure and improve model performance across various dimensions
• Collaborate with research teams to translate emerging techniques into production-ready implementations
• Debug complex issues in training pipelines and model behavior
• Help establish best practices for reliable, reproducible model post-training

You may be a good fit if you:

• Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent priorities
• Adapt quickly to changing priorities
• Maintain clarity when debugging complex, time-sensitive issues
• Have strong software engineering skills with experience building complex ML systems
• Are comfortable working with large-scale distributed systems and high-performance computing
• Have experience with training, fine-tuning, or evaluating large language models
• Can balance research exploration with engineering rigor and operational reliability
• Are adept at analyzing and debugging model training processes
• Enjoy collaborating across research and engineering disciplines
• Can navigate ambiguity and make progress in fast-moving research environments

Strong candidates may also:

• Have experience with LLMs
• Have a keen interest in AI safety and responsible deployment

Logistics

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
• 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.

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Responsibilities:

• Conduct research into the science of converting compute into intelligence
• Independently lead small research projects while collaborating with team members on larger initiatives
• Design, run, and analyze scientific experiments to advance our understanding of large language models
• Optimize training infrastructure to improve efficiency and reliability
• Develop dev tooling to enhance team productivity

You may be a good fit if you:

• Have significant software engineering experience and a proven track record of building complex systems
• Hold an advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
• Are proficient in Python and experienced with deep learning frameworks
• Are results-oriented with a bias towards flexibility and impact
• Enjoy pair programming and collaborative work, and are willing to take on tasks outside your job description to support the team
• View research and engineering as two sides of the same coin, seeking to understand all aspects of the research program to maximize impact
• Care about the societal impacts of your work and have ambitious goals for AI safety and general progress

Strong candidates may have:

• Experience with JAX
• Experience with reinforcement learning
• Experience working on high-performance, large-scale ML systems
• Familiarity with accelerators, Kubernetes, and OS internals
• Experience with language modeling using transformer architectures
• Background in large-scale ETL processes
• Experience with distributed training at scale (thousands of accelerators)

Strong candidates need not have:

• Experience in all of the above areas , we value breadth of interest and willingness to learn over checking every box
• Prior work specifically on language models or transformers; strong engineering fundamentals and ML knowledge transfer well
• An advanced degree , exceptional engineers with strong research instincts are equally encouraged to apply

The annual compensation range for this role is £260,000-£630,000 GBP.

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As a research engineer, you'll partner with the product org to ensure that the vision improvements you deliver impact Claude's performance on real-world tasks. You'll also work with our product org to find solutions to our most vexing API customer challenges related to vision and spatial reasoning.

Strong candidates may also have experience with large-scale pretraining, SL, and RL on language models, deep learning research on images, video, or other modalities, developing complex agentic systems using LLMs, high-performance ML systems (GPUs, TPUs, JAX, PyTorch), and large-scale ETL and data pipeline development.

The annual compensation range for this role is $350,000-$850,000 USD.

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The Mosaic AI organization enables companies to develop AI models and systems using their own data, with technologies ranging from fine-tuning LLMs for enterprise domains, to a platform for building compound AI systems that use retrieval and agents. Mosaic AI is committed to the belief that a company's AI models are just as valuable as any other core IP, and that high-quality AI models should be available to all.

Job description:

Most of the world's data+AI problems lie in enterprise domains, behind closed doors. Our research team's goal is to push the frontier of 'domain adaptation' - how can we develop LLMs and AI systems that work well for custom domains. To do this we are tackling open research problems on a range of topics, from how to scale/automate eval, fine tune with synthetic data, retrieval augmentation, fast/efficient inference and more.

You will work with our research team on projects focused on adapting LLMs and AI systems towards enterprise domains. This may include:

• Adapting, improving, and evaluating a method from the literature.
• Designing an entirely new method for domain adaptation.
• Composing together multiple methods to create new recipes for efficient post-training.
• Evaluation of LLMs and AI systems.

Your qualifications and qualities:

• Required:
• Research experience in and proficiency with the fundamentals of deep learning.
• Pursuing a PhD in computer science or related fields (electrical engineering, neuroscience, physics, math, etc.).
• Proficient software engineering skills, including with PyTorch.

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above.

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

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

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

Responsibilities:

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

Qualifications:

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

Desired:

• Experience with Large Language Models

Travel:

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

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The Anthropic Fellows Program is a 4-month full-time research opportunity that provides funding and mentorship to promising technical talent. As a Reinforcement Learning Fellow, you will work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g., a paper submission).

What to Expect

• 4 months of full-time research
• Direct mentorship from Anthropic researchers
• Access to a shared workspace (in either Berkeley, California or London, UK)
• Connection to the broader AI safety and security research community
• Weekly stipend of $3,850 USD / £2,310 GBP / $4,300 CAD + benefits (these vary by country)
• Funding for compute (~$15k/month) and other research expenses

Responsibilities

As a Reinforcement Learning Fellow, you will be responsible for:

• Building model-based tools to better understand AI training data and improve training data quality
• Conducting research and implementing solutions in areas such as RL algorithms
• Collaborating with other researchers and engineers to advance the state-of-the-art in reinforcement learning

Qualifications

• Fluency in Python programming
• Strong background in a discipline relevant to reinforcement learning (e.g., computer science, mathematics, or physics)
• Experience in areas of research or engineering related to reinforcement learning

Logistics

To participate in the Fellows program, you must have work authorization in the US, UK, or Canada and be located in that country during the program.

How to Apply

Applications and interviews are managed by Constellation, our official recruiting partner for this program. Clicking "Apply here" will redirect you to Constellation's application portal.

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About Us

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

The Role

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

Key responsibilities:

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

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

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

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

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

About You

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

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

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

• Deep understanding of statistics is strongly preferred

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

• Strong knowledge of systems design and data structures

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

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

• A passion for Artificial Intelligence.

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

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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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The Anthropic Fellows Program is a 4-month, full-time research opportunity for individuals to work on empirical AI research and engineering projects. As an AI Security Fellow, you will be part of a team that focuses on reducing catastrophic risks from advanced AI systems.

Responsibilities

• Conduct empirical AI research and engineering projects aligned with Anthropic's research priorities
• Collaborate with mentors and peers to achieve project goals
• Present research findings and results to the team and wider community

Requirements

• Fluency in Python programming
• Strong technical background in computer science, mathematics, or physics
• Ability to implement ideas quickly and communicate clearly

Nice to Have

• Experience with pentesting, vulnerability research, or other offensive security work
• Experience with empirical ML research projects
• Experience with deep learning frameworks and experiment management

Logistics

• To participate in the Fellows program, you must have work authorization in the UK and be located in the UK during the program
• Workspace locations: London and Berkeley
• Visa sponsorship: Not currently available

Application Process

Applications and interviews are managed by Constellation, our official recruiting partner for this program. Clicking 'Apply here' will redirect you to Constellation's application portal.

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Research Engineer, Information Quality

Summary

At Google DeepMind, our research team is dedicated to tackling the most complex challenges in online information quality. We strive to advance the state of the art by developing innovative solutions to detect manipulated media and misleading narratives, ensuring the integrity of digital discourse.

Responsibilities

To succeed in this role, you will need to be passionate about advancing information literacy using machine learning and other computational techniques. You'll join an interdisciplinary team of domain experts, ML researchers, and engineers to research and build systems and tools to assess the trustworthiness of media (images, audio, and videos) on the internet.

Key responsibilities:

• Plan and perform rapid prototyping of machine learning techniques applied to determining authenticity of media information.
• Undertake exploratory analysis to inform experimentation and research directions.
• Engage with product teams to drive the development of our research.
• Implement tools, libraries, and frameworks to speed up and enable new research.
• Report and present research findings, software developments, experimental results, and data analysis clearly and efficiently.
• Collaborate with internal and external scientific domain experts.

Requirements

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

• Master’s degree in Computer Science, Electrical Engineering, Science, or Mathematics, or equivalent experience.
• Applied experience with machine learning, preferably modern deep learning techniques (e.g., Transformers, Diffusion, LLMs).
• Programming experience.
• Quantitative skills in math and statistics.
• Experience exploring, analysing and visualising data.

Preferred Qualifications

In addition, the following would be an advantage:

• Experience in multimodal learning, including the training and deployment of large-scale models.
• Experience developing AI agents.
• Experience with Large Language Models, prompt engineering, few-shot learning, post-training techniques, and evaluations.
• A proven track record of research or engineering achievements, such as publications in peer-reviewed conferences or journals.

Benefits

The US base salary range for this full-time position is between $174,000 USD - $252,000 USD + bonus + equity + benefits.

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This role is pivotal in developing foundational multimodal AI capabilities to understand, generate, and protect human likeness. As a key contributor, you will design and implement cutting-edge models and frameworks, pushing the boundaries of AI to enable foundational capabilities for human-centric understanding and generation.

This is a unique opportunity to contribute to impactful research and advance Google DeepMind's mission towards Artificial General Intelligence (AGI).

Key Responsibilities

• Advance multimodal human representations & understanding: Research and implement novel models and other multimodal techniques for a more holistic understanding of humans across visual, audio, and textual data.
• Conduct applied research: Conduct experimental research cycles from hypothesis to deployment.
• Drive technical projects: Take ownership of substantial technical projects within the effort, from ideation and design to implementation and evaluation, often involving cross-functional collaboration.
• Contribute to Infrastructure: Inform and contribute to the development of scalable and efficient research infrastructure for multimodal human understanding models and datasets.
• Design and execute strategies for tuning and adapting VLMs and other foundation models for specific tasks

Requirements

• PhD degree in Computer Science, Machine Learning, or a related technical field with 3+ years of relevant experience.
• Experience in developing machine learning models, such as audio & speech-visual models.
• Experience in working with and tuning large-scale vision language models.
• Strong programming skills in Python and experience with at least one major deep learning framework (e.g., JAX)
• Experience conducting independent research and development, including experimental design, implementation, and analysis.

Salary

The US base salary range for this full-time position is between $174,000 USD - $252,000 USD + bonus + equity + benefits.

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

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

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

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

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

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

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

Responsibilities:

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

Requirements:

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

Preferences:

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

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

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

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

Responsibilities:

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

Required Qualifications:

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

Preferred Qualifications:

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

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

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

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

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

Responsibilities:

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

Requirements:

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

Preferences:

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

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

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

Responsibilities:

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

Required Qualifications:

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

Preferred Qualifications:

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

$182,720 - $274,080 a year

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As a member of our team, you'll design and implement novel computer vision algorithms from scratch, optimised for real-time performance. You'll develop and maintain C++-based CV pipelines as part of autonomous mission systems, collaborate with a multidisciplinary team of AI, robotics, and optical engineers to deliver reliable edge solutions, and support the integration of deep learning models into broader CV systems.

In this role, you'll have the opportunity to stay across current academic research and emerging techniques in computer vision and ML, and contribute to the development of custom algorithms, not just apply libraries.

Why Shield AI?

• Build mission-critical vision and autonomy systems that make a real-world impact.
• Collaborate with some of the best minds in AI, autonomy, and defence technology.
• Hybrid role based in our Port Melbourne office.
• Salary + equity for permanent roles, with a strong career development pathway.

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

Responsibilities:

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

Requirements:

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

Useful Skills:

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

Physical Demands:

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

Benefits:

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

Additional Information:

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

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

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

Responsibilities:

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

Requirements:

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

Useful Skills:

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

Physical Demands:

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

Benefits:

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

Additional Information:

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

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

Role Summary

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

Responsibilities

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

Requirements

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

What we offer

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

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Responsibilities

• You'll individually help deploy into production use cases with a considerable business impact across various industries.
• You'll work on state-of-the-art GenAI applications from consumer products to industrial use cases, driving technological transformation with our customers.
• You'll work in collaboration with our researchers, other AI engineers, product engineers on complex customer projects involving fine-tuning, state-of-the-art LLM applications, and contributing to our open-source codebases.
• You'll холод involved in pre-sales calls to understand potential clients' needs, challenges, and aspirations.
• You will provide technical guidance on our products and explain Mistral technologies to various stakeholders.
• Your collaboration with our product and science team to improve continuously our product and model capabilities based on customers' 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.
• You have 2+ years as a technical individual contributor (data scientist or software engineer) on AI-based products.
• You have proven experience in AI or machine learning product implementation with APIs, back-end and front-end interfaces.
• You have experience in Fine Tuning LLMs, tackling advanced RAG or agentic use cases.
• 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:

• Contributed to open-source projects in particular in the space of LLMs.
• Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect or Technical Product Manager.
• You have experience with deep learning with PyTorch

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Mistral AI is seeking a Applied AI Engineer to facilitate the adoption of its products among customers and collaborate with them to address complex technical challenges. The Applied AI Engineer will be an integral part of our Applied AI Engineering team, which is dedicated to driving the successful deployment of Mistral AI products.

Responsibilities

• Onboard customers on our products and APIs, providing guidance on prompting, evaluation, and fine-tuning, and ensuring the best production integration with back-end and front-end interfaces.

• Work on state-of-the-art GenAI applications from consumer products to industrial use cases, driving with our customers a crucial technological transformation.

• Individually help deploy into production use cases with a considerable business impact across various industries.

• Collaborate with our researchers, other AI engineers, product engineers on our most complex customer projects involving complex fine-tuning, state-of-the-art LLM applications, and contributing to our open source codebases for tasks such as inference and fine-tuning.

• Involved in pre-sales calls to understand potential clients' needs, challenges, and aspirations. Provide technical guidance on our products and explain Mistral technologies to various stakeholders.

• Collaboration with our product and science team to improve continuously our product and model capabilities based on customers' feedback

About You

• You are fluent in English.

• You hold a PhD / master in AI / data science.

• You have 2+ years as a technical individual contributor (data scientist or software engineer) on AI-based products.

• You have experience in Fine Tuning LLMs, tackling advanced RAG or agentic use cases.

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

• You're experienced with building and deploying LLMs or NLP applications.

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

• You have strong technical coding skills in Python.

• You have experience with deep learning with Pytorch.

• You have experience with agents framework such as Langchain, vector DBs.

Benefits

• Competitive salary and bonus structure.

• Generous Equity.

• Health: Competitive Healthcare program (Medical Provider: Blueshield of California 100% coverage for employee, 75% for dependents).

• Pension: 401K (6% matching).

• PTO: 18 days.

• Transportation: Reimburse office parking charges, or $120/month for public transport.

• Coaching: we offer Betterup coaching on a voluntary basis.

• Sport: $120/month reimbursement for gym membership.

• Meal stipend: $400 monthly allowance for meals (solution might evolve as we grow bigger).

• Visa sponsorship

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We're seeking an Applied AI Engineer to facilitate the adoption of our products among customers and collaborate with them to address complex technical challenges.

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.

Our team combines deep ML expertise with strong customer engagement skills, operating like startup CTOs who own end-to-end project execution.

By joining the team, you'll bridge the gap between cutting-edge AI research and real-world enterprise applications, ensuring our solutions are robust, scalable, and aligned with both customer needs and Mistral's technological vision.

Responsibilities

• You'll individually help deploy into production use cases with a considerable business impact across various industries.
• You'll work on state-of-the-art GenAI applications from consumer products to industrial use cases, driving with our customers a crucial technological transformation.
• You'll work in collaboration with our researchers, other AI engineers, product engineers on our most complex customer projects involving complex fine-tuning, state-of-the-art LLM applications, and contributing to our open-source codebases for tasks such as inference and fine-tuning.
• You'll be involved in pre-sales calls to understand potential clients' needs, challenges, and aspirations. You will provide technical guidance on our products and explain Mistral technologies to various stakeholders.
• Your collaboration with our product and science team to improve continuously our product and model capabilities based on customers' 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.
• You have 2+ years as a technical individual contributor (data scientist or software engineer) on AI-based products.
• You have proven experience in AI or machine learning product implementation with APIs, back-end and front-end interfaces.
• You have experience in Fine Tuning LLMs, tackling advanced RAG or agentic use cases.
• 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:

• Contributed to open-source projects in particular in the space of LLMs.
• Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect or Technical Product Manager.
• You have experience with deep learning with Pytorch

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

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

Responsibilities:

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

Requirements:

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

Nice to have:

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

Benefits and additional information:

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

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

Key responsibilities include:

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

Requirements include:

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

Nice to have qualifications include:

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

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You should have a deep understanding of generative AI, hands-on experience serving generative deep learning models in production settings, and a track record of working directly with customers, iterating on solutions, and providing tailored support. Proficiency in Python and intuitive understanding of API integrations are also essential. Excellent communication skills, honed through collaborating with non-technical stakeholders, are necessary to adapt your message depending on who's in the room.

Prior experience finetuning diffusion models, working with customization tools like ComfyUI, and contributing to open-source projects in the diffusion model space are highly valued. Deploying models on cloud platforms using state-of-the-art serving infrastructure is also desirable.

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

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

Key Responsibilities:

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

About You:

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

Requirements:

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

Preferred Qualifications:

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

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

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

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

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

Responsibilities:

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

Requirements:

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

Preferred Qualifications:

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

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

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

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

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

Responsibilities

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

Minimum Qualifications

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

Preferred Qualifications

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

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Responsibilities:

• Partner with Verticals PM, engineering, and UX to develop data-driven product strategies
• Translate ambiguous questions into well-defined problems, design experiments, and analyse large complex datasets for insights
• Develop and implement novel, goal-oriented metrics
• Build and deploy statistical/ML models to understand our users, enhance product capabilities and personalise user experience
• Communicate findings & recommendations to stakeholders, including executives
• Champion data-driven culture by feeding user engagement insights back into models
• Collaborate with the GenAI team on model quality and feature adoption
• Act as a technical leader for a global team, guiding junior members on complex analyses and upholding best practices to ensure high-quality, impactful work

Requirements:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field
• 5 years of experience with analysis applications (e.g., extracting insights, performing statistical analysis, or solving business problems), and coding (e.g., Python, R, SQL) or 2 years of experience with a Master's degree
• 2 years of work experience identifying opportunities for business/product improvement and then defining/measuring the success of those initiatives
• A bias for action and a relentless drive to build something great
• Strong business acumen and a strategic mindset
• Deep technical expertise
• Exceptional communication and presentation skills
• A collaborative and influential partner
• A commitment to user trust and privacy

Preferred qualifications include a Master's degree in a relevant field and experience working with machine learning and statistical modelling tools.

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

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

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

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This role requires a passion for understanding and modeling the interactions between AI and society, a strong awareness of the AI alignment and safety landscape, and a penchant for developing novel ideas, methods, interfaces, and tools.

As a Research Scientist at Google DeepMind, you will join a team working to supercharge exploration, assessment, and steering of evolving AI behaviours, with a focus on subjective and creative tasks. You will tackle the underlying research questions to improve collaborative specification of alignment objectives and assessment of adherence to desired behaviours.

Key responsibilities include generating new ideas, executing cutting-edge ideas, communicating research findings, collaborating with other researchers, and driving technical projects.

To be successful in this role, you will need a PhD degree in Computer Science, Machine Learning, or a related technical field, a strong publication record in top machine learning conferences, and demonstrated hands-on experience in developing multimodal AI models and systems.

In addition, experience with large-scale vision language models, fine-tuning and post-training LLMs using RL, and developing agentic AI solutions to complex problems would be an advantage.

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

Responsibilities:

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

About you:

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

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

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

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

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

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

In addition, the following would be an advantage:

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

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

Key responsibilities include:

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

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

Requirements include:

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

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

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We are seeking 2 Research Engineers for the Frontier Safety Risk Assessment team within the AGI Safety and Alignment Team.

As a Research Engineer, you will contribute novel research towards our ability to measure and assess risk from frontier models. This might include:

• Identifying new risk pathways within current areas (loss of control, ML R&D, cyber, CBRN, harmful manipulation) or in new ones;
• Conceiving of, designing, and developing new ways to measure pre-mitigation and post-mitigation risk;
• Forecasting and scenario planning for future risks which are not yet material.

Your work will involve complex conceptual thinking as well as engineering. You should be comfortable with research that is uncertain, under-constrained, and which does not have an achievable “right answer”. You should also be skilled at engineering, especially using Python, and able to rapidly familiarise yourself with internal and external codebases. Lastly, you should be able to adapt to pragmatic constraints around compute and researcher time that require us to prioritise effort based on the value of information.

Although this job description is written for a Research Engineer, all members of this team are better thought of as members of technical staff. We expect everyone to contribute to the research as well as the engineering and to be strong in both areas.

The role will mostly depend on your general ability to assess and manage future risks, rather than from specialist knowledge within the risk domains, but insofar as specialist knowledge is helpful, knowledge in ML R&D and loss of control as risk domains are likely the most valuable.

About You

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

• You have extensive research experience with deep learning and/or foundation models (for example, but not necessarily, a PhD in machine learning).
• You are adept at generating ideas and designing experiments, and implementing these in Python with real AI systems.
• You are keen to address risks from foundation models, and have thought about how to do so. You plan for your research to impact production systems on a timescale between “immediately” and “a few years”.
• You are excited to work with strong contributors to make progress towards a shared ambitious goal.
• With strong, clear communication skills, you are confident engaging technical stakeholders to share research insights tailored to their background.

In addition, any of the following would be an advantage:

• Experience in areas such as frontier risk assessment and/or mitigations, safety, and alignment.
• Engineering experience with LLM training and inference.
• PhD in Computer Science or Machine Learning related field.
• A track record of publications at venues such as NeurIPS, ICLR, ICML, RL/DL, EMNLP, AAAI and UAI.
• Experience with collaborating or leading an applied research project.

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

At Google DeepMind, we want employees and their families to live happier and healthier lives, both in and out of work, and our benefits reflect that. Some select benefits we offer: enhanced maternity, paternity, adoption, and shared parental leave, private medical and dental insurance for yourself and any dependents, and flexible working options. We strive to continually improve our working environment, and provide you with excellent facilities such as healthy food, an on-site gym, faith rooms, terraces etc.

We are also open to relocating candidates and offer a bespoke service and immigration support to make it as easy as possible (depending on eligibility).

The US base salary range for this full-time position is between $136,000 - $245,000 + bonus + equity + benefits. Your recruiter can share more about the specific salary range for your targeted location during the hiring process.

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

Responsibilities

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

Requirements

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

Benefits

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

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

Role Summary

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

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

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

Responsibilities

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

Requirements

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

What We Offer

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

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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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We are seeking an Applied AI Engineer to facilitate the adoption of our products among customers and collaborate with them to address complex technical challenges. Our team combines deep ML expertise with strong customer engagement skills, operating like startup CTOs who own end-to-end project execution.

What you will do

• You'll individually help deploy into production use cases with a considerable business impact across various industries.
• You'll work on state-of-the-art GenAI applications from consumer products to industrial use cases, driving with our customers a crucial technological transformation.
• You'll work in collaboration with our researchers, other AI engineers, product engineers on our most complex customer projects involving complex fine-tuning, state-of-the-art LLM applications, and contributing to our open-source codebases for tasks such as inference and fine-tuning.
• You'll be involved in pre-sales calls to understand potential clients' needs, challenges, and aspirations. You will provide technical guidance on our products and explain Mistral technologies to various stakeholders.
• Your collaboration with our product and science team to improve continuously our product and model capabilities based on customers’ 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
• You have 2+ years as a technical individual contributor (data scientist or software engineer) on AI-based products
• You have proven experience in AI or machine learning product implementation with APIs, back-end and front-end interfaces.
• You have experience in Fine Tuning LLMs, tackling advanced RAG or agentic use cases
• 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:

• Contributed to open-source projects in particular in the space of LLMs
• Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect or Technical Product Manager
• You have experience with deep learning with Pytorch

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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 company with teams distributed between France, USA, UK, Germany, and Singapore. Our comprehensive AI platform is designed to meet enterprise needs, whether on-premises or in cloud environments.

About The Job

Mistral AI is seeking a Applied AI Engineer to facilitate the adoption of its products among customers and collaborate with them to address complex technical challenges. The Applied AI Engineer will be an integral part of our Applied AI Engineering team, which is dedicated to driving the successful deployment of Mistral AI products.

Responsibilities

• Onboard customers on our products and APIs, providing guidance on prompting, evaluation, and fine-tuning, and ensuring the best production integration with back-end and front-end interfaces.

• Work on state-of-the-art GenAI applications from consumer products to industrial use cases, driving with our customers a crucial technological transformation.

• Individually help deploy into production use cases with a considerable business impact across various industries.

• Collaborate with our researchers, other AI engineers, product engineers on our most complex customer projects involving complex fine-tuning, state-of-the-art LLM applications, and contributing to our open-source codebases.

• Involved in pre-sales calls to understand potential clients' needs, challenges, and aspirations. Provide technical guidance on our products and explain Mistral technologies to various stakeholders.

About You

• You are fluent in English.

• You hold a PhD / master in AI / data science.

• You have 2+ years as a technical individual contributor (data scientist or software engineer) on AI-based products.

• You have experience in Fine Tuning LLMs, tackling advanced RAG or agentic use cases.

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

• You're experienced with building and deploying LLMs or NLP applications.

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

• You have strong technical coding skills in Python.

• You have experience with deep learning with Pytorch.

• You have experience with agents framework such as Langchain, vector DBs.

Benefits

• Competitive cash salary and equity

• Health Insurance

• Sport: $90 for gym membership allowance

• Food: $200 monthly allowance for meals (solution might evolve as we grow bigger)

• Transportation: $120/month for public transport or Parking charges reimbursed

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We are seeking an Applied AI Engineer to facilitate the adoption of our products among customers and collaborate with them to address complex technical challenges. As a member of our Applied AI team, you will bridge the gap between cutting-edge AI research and real-world enterprise applications, ensuring our solutions are robust, scalable, and aligned with both customer needs and our technological vision.

Responsibilities

• Deploy into production use cases with a considerable business impact across various industries.
• Work on state-of-the-art GenAI applications from consumer products to industrial use cases, driving with our customers a crucial technological transformation.
• Collaborate with our researchers, other AI engineers, product engineers on our most complex customer projects involving complex fine-tuning, state-of-the-art LLM applications, and contributing to our open-source codebases.
• Participate in pre-sales calls to understand potential clients' needs, challenges, and aspirations.
• Provide technical guidance on our products and explain Mistral technologies to various stakeholders.

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.
• You have 2+ years as a technical individual contributor (data scientist or software engineer) on AI-based products.
• You have proven experience in AI or machine learning product implementation with APIs, back-end and front-end interfaces.
• You have experience in Fine Tuning LLMs, tackling advanced RAG or agentic use cases.
• You have deep understanding of concepts and algorithms underlying machine learning and LLMs.
• You have strong technical coding skills in Python.

Ideally You Have:

• Contributed to open-source projects in particular in the space of LLMs.
• Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect or Technical Product Manager.
• You have experience with deep learning with PyTorch.

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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 company 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.

Role Summary

As an Applied Engineering Intern, you will work closely with our Applied AI Engineering team to facilitate the adoption of Mistral AI products among customers and collaborate with them to address complex technical challenges. This role is based in Paris, with an internship duration of 3 to 6 months. We are open to CIFRE programs as a continuation after the internship.

Responsibilities

• Contribute to the deployment of state-of-the-art GenAI applications, driving technological transformation with our customers.

• Collaborate with researchers, AI engineers, and product engineers on complex customer projects.

• Work with the product and science team to continuously improve our product and model capabilities 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 currently pursuing a degree in AI, data science, or a related field from a tier 1 engineering school or university.

• You have strong programming skills in Python.

• You are familiar with machine learning algorithms and natural language processing techniques.

• You hold basic understanding of MLOps and deploying machine learning use cases.

• You have good communication skills with the ability to explain technical concepts to both technical and non-technical audiences.

Ideally You Have:

• Experience with deep learning frameworks such as PyTorch.

• Familiarity with version control systems (e.g., Git) and Linux shell environment.

• Experience working in HPC Environments.

• Publication record in AI or a related field.

Benefits

• Competitive salary

• Food: Daily lunch vouchers

• Sport: Monthly contribution to a Gympass subscription

• Transportation: Monthly contribution to a mobility pass

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Responsibilities:

• Research and develop techniques to GPU-accelerate high-performance workloads at the intersection of AI and financial markets.
• Work directly with other technical experts in their fields to perform in-depth analysis and optimization of complex AI and HPC workloads to ensure the best possible performance on modern CPU and GPU architectures.
• Publish and present discovered optimization techniques in developer blogs or relevant conferences to engage and educate the Developer community.
• Influence the design of next-generation hardware architectures, software, and programming models in collaboration with research, hardware, system software, libraries, and tools teams at NVIDIA.

Requirements:

• An advanced degree in Computer Science, Computer Engineering, or related computationally focused science degree (or equivalent experience).
• 5+ years of relevant work or research experience.
• Direct experience improving the performance of large computational applications used by financial institutions.
• Excellent understanding of linear algebra.
• Programming fluency in C/C++ with a deep understanding of algorithms and software design.
• Hands-on experience with low-level parallel programming, e.g., CUDA, OpenACC, OpenMP, MPI, pthreads, TBB, etc.
• In-depth expertise with CPU/GPU architecture fundamentals.
• Good communication and organization skills, with a logical approach to problem solving, and prioritization skills.

Ways to stand out from the crowd:

• A Master’s or PhD in a relevant field is highly valued.
• Prior work experience in capital markets with exposure to systematic/algorithmic strategies and quantitative trading.
• Experience with parallelizing and optimizing machine learning algorithms like decision trees, time series, and Monte Carlo simulations.
• Deep knowledge of financial data models, pricing/risk simulation algorithms, portfolio optimization, or other financial specific applications/ services.
• Have developed ML/DL techniques in the finance space, such as stock market prediction, fraud detection, portfolio optimization/selection.

You will also be eligible for equity and benefits.

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

What you will be doing:

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

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

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

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

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

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

What we need to see:

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

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

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

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

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

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

• Good programming and debugging skills

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

Ways to stand out from the crowd:

• AWS, GCP or Azure Professional Solution Architect Certification.

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

• System-level experience specifically GPU-based systems

• Experience with Deep Learning at scale

• Familiarity with parallel programming and distributed computing platforms

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The AI Deployment Manager role is a specialist post-sales enablement role focused on delivering high-impact enablement and adoption services across OpenAI's product suite. This role is responsible for designing and delivering technical enablement experiences that support a repeatable adoption framework– driving sustained activation, expanding breadth and depth of usage, and measurable business value across OpenAI's product suite, including ChatGPT Enterprise, Codex, Agents, and the API.

Responsibilities

• Own the technical enablement of OpenAI products, including ChatGPT Enterprise, Codex, Agents, and API capabilities, helping define effective enablement patterns that support adoption across customer segments
• Lead customer training and enablement across the full customer lifecycle, from initial onboarding through expansion, optimization, and long-term adoption
• Design and deliver high-impact training engagements, including onboarding sessions, advanced capability trainings, executive briefings, hackathons, and hands-on workshops for audiences ranging from senior leaders to working teams
• Drive customer activation, sustained usage, and measurable business value through structured enablement and deployment programs designed for durable adoption at scale
• Partner closely with Sales, AI Success Engineers, Solutions Engineering, and Product teams to ensure seamless handoff from pre- to post-sale and consistent customer experience
• Develop and refine reusable training assets, playbooks, and best practices based on patterns observed across customers and regions
• Gather customer feedback from training and enablement engagements, synthesize themes across accounts, and relay insights to internal stakeholders to inform product and program improvements

Requirements

• 4+ years of experience in customer-facing or instructional roles, engaging C-level and senior technical audiences in complex enterprise environments
• Exceptional presentation and communication skills, particularly when conveying the value of technical concepts clearly to senior and executive-level audiences
• Strong technical depth across coding, agents, and APIs, with a practical understanding of how AI systems are built, evaluated, and operated in production, including RAG, evaluation strategies, fine-tuning, and key tradeoffs
• Proven experience leading structured technical trainings, such as API bootcamps, workshops, or enablement sessions, with the ability to design learning journeys, handle live questions, and reason through problems in real time
• Strong ability to connect technical features and capabilities to concrete business outcomes such as productivity, efficiency, cost reduction, risk mitigation, or revenue impact
• Humble attitude, eagerness to help others, and desire to pick up whatever knowledge you're missing to make both your team and our customers succeed
• Comfortable thinking on their feet in live customer settings, adapting quickly to new information, shifting priorities, and real-time questions while maintaining clarity, ownership, and momentum
• Personal commitment to fostering the safe and ethical evolution of AI

About OpenAI

OpenAI is an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

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As a Solutions Architect, you will engage directly with developers, researchers, and data scientists with some of NVIDIA’s most strategic technology customers as well as work directly with business and engineering teams on product strategy.

Key Responsibilities:

• Help cloud customers craft, deploy, and maintain scalable, GPU-accelerated inference pipelines on cloud ML services and Kubernetes for large language models (LLMs) and generative AI workloads.
• Enhance performance tuning using TensorRT/TensorRT-LLM, vLLM, Dynamo, and Triton Inference Server to improve GPU utilization and model efficiency.
• Collaborate with multi-functional teams (engineering, product) and offer technical mentorship to cloud customers implementing AI inference at scale.
• Build custom PoCs for solution that address customer’s critical business needs applying NVIDIA hardware and software technology
• Partner with Sales Account Managers or Developer Relations Managers to identify and secure new business opportunities for NVIDIA products and solutions for ML/DL and other software solutions
• Prepare and deliver technical content to customers including presentations about purpose-built solutions, workshops about NVIDIA products and solutions, etc.
• Conduct regular technical customer meetings for project/product roadmap, feature discussions, and intro to new technologies. Establish close technical ties to the customer to facilitate rapid resolution of customer issues

Requirements:

• BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Statistics, Physics, or other Engineering fields or equivalent experience.
• 3+ Years in Solutions Architecture with a proven track record of moving AI inference from POC to production in cloud computing environments including AWS, GCP, or Azure
• 3+ years of hands-on experience with Deep Learning frameworks such as PyTorch and TensorFlow
• Excellent knowledge of the theory and practice of LLM and DL inference
• Strong fundamentals in programming, optimizations, and software design, especially in Python
• Experience with containerization and orchestration technologies like Docker and Kubernetes, monitoring, and observability solutions for AI deployments
• Knowledge of Inference technologies - NVIDIA NIM, TensorRT-LLM, Dynamo, Triton Inference Server, vLLM, etc
• Proficiency in problem-solving and debugging skills in GPU environments
• Excellent presentation, communication and collaboration skills

Nice to Have:

• AWS, GCP or Azure Professional Solution Architect Certification.
• Experience optimizing and deploying large MoE LLMs at scale
• Active contributions to open-source AI inference projects (e.g., vLLM, TensorRT-LLM Dynamo, SGLang, Triton or similar)
• Experience with Multi-GPU Multi-node Inference technologies like Tensor Parallelism/Expert Parallelism, Disaggregated Serving, LWS, MPI, EFA/Infiniband, NVLink/PCIe, etc
• Experience in developing and integrating monitoring and alerting solutions using Prometheus, Grafana, and NVIDIA DCGM and GPU performance Analysis and tools like NVIDIA Nsight Systems

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