Responsibilities:

• Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments
• Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities
• Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI
• Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows
• Collaborate to translate experimental requirements into production-ready infrastructure
• Develop large scale data pipelines to handle advanced language model training requirements
• Optimize large scale training and inference pipelines for stable and efficient reinforcement learning

You may be a good fit if you:

• Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems
• Are a strong communicator and enjoy working collaboratively
• Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads
• Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale
• Have proven track record of building large-scale data pipelines and distributed storage systems
• Excel at diagnosing and resolving complex infrastructure challenges in production environments
• Can work effectively across the full ML stack from data pipelines to performance optimization
• Have experience collaborating with other researchers to scale experimental ideas
• Thrive in fast-paced environments and can rapidly iterate from experimentation to production

Strong candidates may also have:

• Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.)
• Background in building infrastructure for AI research labs or large-scale ML organizations
• Knowledge of GPU/TPU architectures and language model inference optimization
• Experience with cloud platforms (AWS, GCP) at enterprise scale
• Familiarity with VM and container orchestration
• Experience with workflow orchestration tools and experiment management systems
• History working with large scale reinforcement learning
• Comfort with large scale data pipelines (Beam, Spark, Dask, …)

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

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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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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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As a Research Engineer on our team, you will work end to end across the whole model stack, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on. This may include performance optimization, distributed systems, VM/sandboxing/container deployment, and large scale data pipelines.

Responsibilities:

• Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments
• Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities
• Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI.
• Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows
• Collaborate to translate experimental requirements into production-ready infrastructure
• Develop large scale data pipelines to handle advanced language model training requirements
• Optimize large scale training and inference pipelines for stable and efficient reinforcement learning

You may be a good fit if you:

• Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems
• Are a strong communicator and enjoy working collaboratively
• Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads
• Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale
• Have proven track record of building large-scale data pipelines and distributed storage systems
• Excel at diagnosing and resolving complex infrastructure challenges in production environments
• Can work effectively across the full ML stack from data pipelines to performance optimization
• Have experience collaborating with other researchers to scale experimental ideas
• Thrive in fast-paced environments and can rapidly iterate from experimentation to production

Strong candidates may also have:

• Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.)
• Background in building infrastructure for AI research labs or large-scale ML organizations
• Knowledge of GPU/TPU architectures and language model inference optimization
• Experience with cloud platforms (AWS, GCP) at enterprise scale
• Familiarity with VM and container orchestration.
• Experience with workflow orchestration tools and experiment management systems
• History working with large scale reinforcement learning
• Comfort with large scale data pipelines (Beam, Spark, Dask, …)

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 projects, and we're committed to making a positive impact on the world.

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