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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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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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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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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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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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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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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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'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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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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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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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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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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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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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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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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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:

• Design, implement and maintain highly-optimized communication runtimes for Deep Learning frameworks (e.g. NCCL for TensorFlow/Pytorch) and HPC programming interfaces (e.g. UCX for MPI/OpenSHMEM) on GPU clusters.
• Participate in and contribute to parallel programming interface specifications like MPI/OpenSHMEM.
• Design, implement and maintain system software that enables interactions among GPUs and interactions between GPUs and other system components.
• Create proof-of-concepts to evaluate and motivate extensions in programming models, new designs in runtimes and new features in hardware.

Requirements:

• M.S./Ph.D. degree in CS/CE or equivalent experience.
• 5+ years of relevant experience.
• Excellent C/C++ programming and debugging skills.
• Strong experience with Linux.
• Expert understanding of computer system architecture and operating systems.
• Experience with parallel programming interfaces and communication runtimes.
• Ability and flexibility to work and communicate effectively in a multi-national, multi-time-zone corporate environment.

Nice to Have:

• Deep understanding of technology and passionate about what you do.
• Experience with CUDA programming and NVIDIA GPUs.
• Knowledge of high-performance networks like InfiniBand, iWARP etc.
• Experience with HPC applications.
• Experience with Deep Learning Frameworks such PyTorch, TensorFlow, etc.
• Strong collaborative and interpersonal skills, specifically a proven ability to effectively guide and influence within a dynamic matrix environment.

Benefits:

• Highly competitive salaries.
• Comprehensive benefits package.
• Eligibility for equity.
• Opportunity to work with a world-class engineering team.
• Ability to work in a dynamic matrix environment.
• Opportunity to contribute to cutting-edge technology.
• Flexible work arrangements.
• Professional development opportunities.

How to Apply:

Applications for this job will be accepted at least until March 13, 2026. NVIDIA uses AI tools in its recruiting processes.

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

About the team

We are seeking passionate Research Scientists and Engineers to join our growing Pre-training team in Zurich. We are involved in developing the next generation of large language models. The team primarily focuses on multimodal capabilities: giving LLMs the ability to understand and interact with modalities other than text.

In this role, 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

In this role you will interact with many parts of the engineering and research stacks.

• 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 analyse 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

We encourage you to apply even if you do not believe you meet every single criterion. Because we focus on so many areas, the team is looking for both experienced engineers and strong researchers, and encourage anyone along the researcher/engineer spectrum to apply.

• 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

If you're excited about pushing the boundaries of AI while prioritising safety and ethics, we want to hear from you!

Logistics

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

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

How we're different

We believe that the highest-impact work in AI safety and general progress in the next few years will be done by a single, cohesive team pursuing large-scale AI research projects. We're committed to creating a work environment that is inclusive, diverse, and supportive of our team members' well-being and career growth.

Career Growth

We're committed to helping our team members grow and develop their careers. We offer opportunities for professional development, mentorship, and career advancement. We believe that our team members are the key to our success, and we're committed to supporting their growth and development.

Benefits

We offer a competitive salary and benefits package, including health insurance, retirement savings, and paid time off. We also offer a range of perks, including a generous parental leave policy, flexible work arrangements, and access to cutting-edge technology and tools.

How to Apply

If you're excited about joining our team and contributing to the development of safe, steerable, and trustworthy AI systems, please submit your application. We can't wait to hear from you!

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

Apply using this link. We’re accepting applications on a rolling basis for cohorts starting in July 2026 and beyond. Applications for the May 2026 cohort are now closed.

Anthropic Fellows Program Overview

The Anthropic Fellows Program is designed to accelerate AI safety research and foster research talent. We provide funding and mentorship to promising technical talent - regardless of previous experience - to research the frontier of AI safety for four months.

Fellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). In our previous cohorts, over 80% of fellows produced papers (more below).

We run multiple cohorts of Fellows each year. This application is for cohorts starting in July 2026 and beyond.

What to Expect

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

Mentors, Research Areas, & Past Projects

Fellows will undergo a project selection & mentor matching process. Potential mentors amongst others include:

• Jan Leike
• Sam Bowman
• Sara Price
• Alex Tamkin
• Nina Panickssery
• Trenton Bricken
• Logan Graham
• Jascha Sohl-Dickstein
• Nicholas Carlini
• Joe Benton
• Collin Burns
• Fabien Roger
• Samuel Marks
• Kyle Fish
• Ethan Perez

Our mentors will lead projects in select AI safety research areas, such as:

• Scalable Oversight: Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains.
• Adversarial Robustness and AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios.
• Model Organisms: Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise.
• Model Internals / Mechanistic Interpretability: Advancing our understanding of the internal workings of large language models to enable more targeted interventions and safety measures.
• AI Welfare: Improving our understanding of potential AI welfare and developing related evaluations and mitigations.

On our Alignment Science and Frontier Red Team blogs, you can read about past projects, including:

• AI agents find $4.6M in blockchain smart contract exploits: Winnie Xiao and Cole Killian, mentored by Nicholas Carlini and Alwin Peng
• Subliminal Learning: Language Models Transmit Behavioral Traits via Hidden Signals in Data: Alex Cloud and Minh Le, et al., mentors including Samuel Marks and Owain Evans
• Open-source circuits: Michael Hanna and Mateusz Piotrowski with mentorship from Emmanuel Ameisen and Jack Lindsey

For a full list of representative projects for each area, please see these blog posts: Introducing the Anthropic Fellows Program for AI Safety Research, Recommendations for Technical AI Safety Research Directions.

You may be a good fit if you

• Are motivated by reducing catastrophic risks from advanced AI systems
• Are excited to transition into full-time empirical AI safety research and would be interested in a full-time role at Anthropic

Please note: We do not guarantee that we will make any full-time offers to fellows. However, strong performance during the program may indicate that a Fellow would be a good fit here at Anthropic. In previous cohorts, over 40% of fellows received a full-time offer, and we’ve supported many more to go on to do great work on safety at other organisations.

• Have a strong technical background in computer science, mathematics, physics, cybersecurity, or related fields
• Thrive in fast-paced, collaborative environments
• Can implement ideas quickly and communicate clearly

Strong candidates may also have:

• Experience with empirical ML research projects
• Experience working with Large Language Models
• Experience in one of the research areas mentioned above
• Experience with deep learning frameworks and experiment management
• Track record of open-source contributions

Candidates must be:

• Fluent in Python programming
• Available to work full-time on the Fellows program for 4 months

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

Interview process

The interview process will include an initial application & references check, technical assessments & interviews, and a research discussion.

Compensation

The expected base stipend for this role is 3,850 USD / 2,310 GBP / 4,300 CAD per week, with an expectation

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

AI Security at Anthropic

We believe we are at an inflection point for AI's impact on cybersecurity. Models are now useful for cybersecurity tasks in practice: for example, Claude can now outperform human teams in some cybersecurity competitions and help us discover vulnerabilities in our own code.

We are looking for researchers and engineers to help us accelerate defensive use of AI to secure code and infrastructure.

Anthropic Fellows Program Overview

The Anthropic Fellows Program is designed to accelerate AI security and safety research, and foster research talent. We provide funding and mentorship to promising technical talent - regardless of previous experience - to research the frontier of AI security and safety for four months.

Fellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). In our previous cohorts, over 80% of fellows produced papers (more below).

We run multiple cohorts of Fellows each year. This application is for cohorts starting in July 2026 and beyond.

What to Expect

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

Mentors, Research Areas, & Past Projects

Fellows will undergo a project selection & mentor matching process. Potential mentors include:

• Nicholas Carlini
• Keri Warr
• Evyatar Ben Asher
• Keane Lucas
• Newton Cheng

On our Alignment Science and Frontier Red Team blogs, you can read about some past Fellows projects, including:

• AI agents find $4.6M in blockchain smart contract exploits: Winnie Xiao and Cole Killian, mentored by Nicholas Carlini and Alwin Peng
• Strengthening Red Teams: A Modular Scaffold for Control Evaluations: Chloe Loughridge et al., mentored by Jon Kutasov and Joe Benton

You may be a good fit if you

• Are motivated by reducing catastrophic risks from advanced AI systems
• Are excited to transition into full-time empirical AI safety research and would be interested in a full-time role at Anthropic

Please note:

We do not guarantee that we will make any full-time offers to fellows. However, strong performance during the program may indicate that a Fellow would be a good fit here at Anthropic. In previous cohorts, over 40% of fellows received a full-time offer, and we’ve supported many more to go on to do great work on safety at other organisations.

Strong candidates may also have:

• Contributed to open-source projects in LLM- or security-adjacent repositories
• Demonstrated success in bringing clarity and ownership to ambiguous technical problems
• Experience with pentesting, vulnerability research, or other offensive security
• A history demonstrating desire to do the 'dirty work' that results in high-quality outputs
• Reported CVEs, or been awarded for bug bounty vulnerabilities
• Experience with empirical ML research projects
• Experience with deep learning frameworks and experiment management

Candidates must be:

• Fluent in Python programming
• Available to work full-time on the Fellows program for 4 months

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.

Interview process

The interview process will include an initial application & references check, technical assessments & interviews, and a research discussion.

Compensation

The expected base stipend for this role is 3,850 USD / 2,310 GBP / 4,300 CAD per week, with an expectation of 40 hours per week, for 4 months (with possible extension).

Logistics

Logistics Requirements: 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.

Workspace Locations: We have designated shared workspaces in London and Berkeley where fellows will work from and mentors will visit. We are also open to remote fellows in the UK, US, or Canada. We will ask you about your availability to work from Berkeley or London (full- or part-time) during the program.

Visa Sponsorship: We are not currently able to sponsor visas for fellows. 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.

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

About the role

Anthropic is seeking a Research Engineer/Scientist to join the Science of Scaling team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. You'll contribute across the entire stack, from low-level optimizations to high-level algorithm and experimental design, balancing research goals with practical engineering constraints.

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 analyse scientific experiments to advance our understanding of large language models
• Optimise 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 maximise 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

Logistics

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

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

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

How we're different

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

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including

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

Key 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 analyse 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:

• Advanced degree (MS or PhD) 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 experience with deep learning frameworks (PyTorch preferred)

• Familiarity with large-scale machine learning, particularly in the context of language models

• Ability to balance research goals with practical engineering constraints

• Strong problem-solving skills and a results-oriented mindset

• Excellent communication skills and ability to work in a collaborative environment

• Care about the societal impacts of your work

Preferred Experience:

• Work on high-performance, large-scale ML systems

• Familiarity with GPUs, Kubernetes, and OS internals

• Experience with language modelling using transformer architectures

• Knowledge of reinforcement learning techniques

• Background in large-scale ETL processes

You'll thrive in this role if you:

• Have significant software engineering experience

• Are results-oriented with a bias towards flexibility and impact

• Willingly 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

• Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research

• View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximise the impact of your insights

• Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term.

Sample Projects:

• Optimising the throughput of novel attention mechanisms

• Comparing compute efficiency of different Transformer variants

• Preparing large-scale datasets for efficient model consumption

• Scaling distributed training jobs to thousands of GPUs

• Designing fault tolerance strategies for our training infrastructure

• Creating interactive visualisations of model internals, such as attention patterns

Benefits:

At Anthropic, we are committed to fostering a diverse and inclusive workplace. We strongly encourage applications from candidates of all backgrounds, including those from underrepresented groups in tech.

Logistics:

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

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

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

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

• Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from https://job-boards.greenhouse.io.

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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.

_Note: For this role, we conduct all interviews in Python. 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 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 analysing 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 listed below.

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

Annual Salary:

$350,000 - $500,000USD

Logistics

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

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

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

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

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

How we're different

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

XML job scraping automation by YubHub

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

Key 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 analyse 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:

• Advanced degree (MS or PhD) 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 experience with deep learning frameworks (PyTorch preferred)
• Familiarity with large-scale machine learning, particularly in the context of language models
• Ability to balance research goals with practical engineering constraints
• Strong problem-solving skills and a results-oriented mindset
• Excellent communication skills and ability to work in a collaborative environment
• Care about the societal impacts of your work

Preferred Experience:

• Work on high-performance, large-scale ML systems
• Familiarity with GPUs, Kubernetes, and OS internals
• Experience with language modelling using transformer architectures
• Knowledge of reinforcement learning techniques
• Background in large-scale ETL processes

You'll thrive in this role if you:

• Have significant software engineering experience
• Are results-oriented with a bias towards flexibility and impact
• Willingly 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
• Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research
• View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximise the impact of your insights
• Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term.

Sample Projects:

• Optimising the throughput of novel attention mechanisms
• Comparing compute efficiency of different Transformer variants
• Preparing large-scale datasets for efficient model consumption
• Scaling distributed training jobs to thousands of GPUs
• Designing fault tolerance strategies for our training infrastructure
• Creating interactive visualisations of model internals, such as attention patterns

At Anthropic, we are committed to fostering a diverse and inclusive workplace. We strongly encourage applications from candidates of all backgrounds, including those from underrepresented groups in tech.

If you're excited about pushing the boundaries of AI while prioritising safety and ethics, we want to hear from you!

The annual compensation range for this role is listed below.

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

Annual Salary:

$350,000 - $850,000USD

XML job scraping automation by YubHub

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.

_Note: For this role, we conduct all interviews in Python. 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 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

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

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

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

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

How we're different

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

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

Come work with us!

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Microsoft AI are looking for a talented Member of Technical Staff, Multimodal Infrastructure to help build the next wave of capabilities of our personalized AI assistant, Copilot. We're looking for someone who will bring an abundance of positive energy, empathy, and kindness to the team every day, in addition to being highly effective.

About the Role

We're looking for someone who will design, develop and maintain large-scale multimodal data processing pipelines, model pretraining and post-training frameworks, and model inference and serving frameworks. You will work closely with research scientists and product engineers on multimodal data processing, model training, inference and serving tasks. As a contributing member of the core group of engineers, you would also bring to the table best practices driving architectural changes and influence roadmap of relevant software and hardware components.

Accountabilities

• Design, develop and maintain large-scale multimodal data processing pipelines.
• Design, develop and maintain large-scale multimodal model pretraining and post-training frameworks.
• Design, develop and maintain large-scale multimodal model inference and serving frameworks.

The Candidate we're looking for

Experience:

• Bachelor's Degree in Computer Science, or related technical discipline AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.

Technical skills:

• Strong proficiency in distributed data processing infra (resource utilization management, fault tolerance, ray & spark) and CPU/GPU batch processing optimizations.
• Experience with state-of-art model inference and serving frameworks.
• Experience with image/video/audio data processing.
• Experience with common data formats for efficient I/O.

Personal attributes:

• Enjoy working in a fast-paced, design-driven, product development cycle.
• Embody our Culture and Values.

Benefits

• Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.
• Comprehensive health and wellbeing benefits.
• Professional development opportunities.
• Financial benefits (bonus, equity, pension, etc.).

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Microsoft are looking for a talented Senior Software Engineer at their Suzhou office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising the search engine and online advertising ecosystem. You'll work directly with leadership to shape the company's direction in the search and advertising markets.

About the Role

The R&D of Search Ads aims to build an online advertising ecosystem of users, advertisers, and the search engine. Bing Search Ads Understanding team is chartered to deliver world class algorithm using web scale data. Our mission is to drive user satisfaction, advertiser ROI and Bing revenue. A core challenge is to match advertisers’ “Ad display” and users’ “query” by build an intelligent system to really understand the users need. This is a very hard problem that demands the most advanced AI models and sophisticated engineering systems. Join us to work on projects highly strategic to Bing search in a fun and fast-paced environment!

Accountabilities

• Design, develop, and maintain high-performance software in C/C++ and Python, including GPU programming with CUDA, ROCm, or Triton.
• Optimize model inference and training pipelines for speed, throughput, memory efficiency, and cost across GPU platforms.

The Candidate we're looking for

Experience:

• Bachelor’s Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, Python, CUDA, or ROCm OR equivalent experience.

Technical skills:

• Practical experience writing new GPU kernels, going beyond experience of GPU workloads with existing library kernels.

Personal attributes:

• Cross-team collaboration skills and the desire to collaborate in a team of researchers and developers.

Benefits

• Work on projects highly strategic to Bing search in a fun and fast-paced environment.
• Collaborate with platform teams to integrate and tune solutions on emerging accelerator stacks and rapidly evolving toolchains.
• Partner with internal and external stakeholders to translate requirements into scalable performance features and optimizations for state-of-the-art models.

XML job scraping automation by YubHub

Microsoft are looking for a talented Senior Software Engineer at their Beijing office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising the search engine and online advertising ecosystem. You'll work directly with leadership to shape the company's direction in the search engine and online advertising markets.

About the Role

The R&D of Search Ads aims to build an online advertising ecosystem of users, advertisers, and the search engine. Bing Search Ads Understanding team is chartered to deliver world class algorithm using web scale data. Our mission is to drive user satisfaction, advertiser ROI and Bing revenue. A core challenge is to match advertisers’ “Ad display” and users’ “query” by build an intelligent system to really understand the users need. This is a very hard problem that demands the most advanced AI models and sophisticated engineering systems. Join us to work on projects highly strategic to Bing search in a fun and fast-paced environment!

Accountabilities

• Design, develop, and maintain high-performance software in C/C++ and Python, including GPU programming with CUDA, ROCm, or Triton.
• Optimize model inference and training pipelines for speed, throughput, memory efficiency, and cost across GPU platforms.

The Candidate we're looking for

Experience:

• Bachelor’s Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, Python, CUDA, or ROCm OR equivalent experience.

Technical skills:

• Practical experience writing new GPU kernels, going beyond experience of GPU workloads with existing library kernels.

Personal attributes:

• Cross-team collaboration skills and the desire to collaborate in a team of researchers and developers.

Benefits

• Work on projects highly strategic to Bing search in a fun and fast-paced environment.
• Collaborate with platform teams to integrate and tune solutions on emerging accelerator stacks and rapidly evolving toolchains.
• Partner with internal and external stakeholders to translate requirements into scalable performance features and optimizations for state-of-the-art models.

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Microsoft AI are looking for a talented Principal Applied Scientist at their Hyderabad office. This role sits at the heart of shaping and improving sports experiences on Bing. You will own end-to-end outcomes: turn ambiguous product questions into measurable hypotheses, define success metrics, design experiments, and ship data-driven ML solutions that move customer and business KPIs.

About the Role

We are seeking an experienced, self-directed Principal Applied Data Scientist to shape and improve sports experiences on Bing. In this individual contributor role, you will own end-to-end outcomes: turn ambiguous product questions into measurable hypotheses, define success metrics, design experiments, and ship data-driven ML solutions that move customer and business KPIs. You will apply modern NLP/IR and multimodal methods—including training and adapting Large Language Models (LLMs) and Small Language Models (SLMs)—to deliver accurate, fresh, and helpful sports answers and discovery experiences at global scale.

Accountabilities

• Define north-star metrics and guardrails; build measurement plans, offline scorecards, and online A/B tests; interpret results and drive clear ship/iterate decisions.
• Build, train, and adapt LLM/SLM solutions for sports scenarios (prompting, supervised fine-tuning, distillation, and domain adaptation), using disciplined evaluation and error analysis to improve quality, latency, and cost.

The Candidate we're looking for

Experience:

• 6+ years related experience (e.g., statistics, predictive analytics, research).

Technical skills:

• Proficiency in Python (and one of C++/C#/Java preferred) and deep learning frameworks (e.g., PyTorch, TensorFlow); experience with distributed training/inference is a plus.

Personal attributes:

• Demonstrated ability to lead through influence as a senior IC: independently define strategy, drive execution across teams, and deliver measurable impact.

Benefits

• Starting January 26, 2026, Microsoft AI employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week.

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What you'll do

Develop APIs for AI inference that will be used by both internal and external customers.

• Develop APIs for AI inference that will be used by both internal and external customers
• Benchmark and address bottlenecks throughout our inference stack
• Improve the reliability and observability of our systems and respond to system outages
• Explore novel research and implement LLM inference optimizations

What you need

• Experience with ML systems and deep learning frameworks (e.g. PyTorch, TensorFlow, ONNX)
• Familiarity with common LLM architectures and inference optimization techniques (e.g. continuous batching, quantization, etc.)
• Understanding of GPU architectures or experience with GPU kernel programming using CUDA

Why this matters

As an AI Inference engineer, you will play a critical role in the development and deployment of our machine learning models. Your work will have a direct impact on the performance and reliability of our systems, and will help us to continue to innovate and improve our products.

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What you'll do

As an AI Inference Engineer at Perplexity, you will have the opportunity to work on large-scale deployment of machine learning models for real-time inference. You will be responsible for developing APIs for AI inference that will be used by both internal and external customers.

• Develop APIs for AI inference that will be used by both internal and external customers
• Benchmark and address bottlenecks throughout our inference stack
• Improve the reliability and observability of our systems and respond to system outages
• Explore novel research and implement LLM inference optimisations

What you need

To be successful in this role, you will need to have experience with ML systems and deep learning frameworks (e.g. PyTorch, TensorFlow, ONNX), familiarity with common LLM architectures and inference optimisation techniques (e.g. continuous batching, quantisation, etc.), and understanding of GPU architectures or experience with GPU kernel programming using CUDA.

• Experience with ML systems and deep learning frameworks (e.g. PyTorch, TensorFlow, ONNX)
• Familiarity with common LLM architectures and inference optimisation techniques (e.g. continuous batching, quantisation, etc.)
• Understanding of GPU architectures or experience with GPU kernel programming using CUDA

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