AI ML Engineer Jobs at Anthropic with Visa Sponsorship
Anthropic hires AI ML Engineers to build and align frontier AI systems, with roles spanning model training, evaluation, and safety research. The company has an established track record of sponsoring work visas for this function, making it a realistic target for international candidates in machine learning.
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Get Access To All JobsAbout Anthropic
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 Teams
Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.5 and Opus 4.5. Our work spans several key areas:
- Developing systems that enable models to use computers effectively
- Advancing code generation through reinforcement learning
- Pioneering fundamental RL research for large language models
- Building scalable RL infrastructure and training methodologies
- Enhancing model reasoning capabilities
We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.
About The Role
As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.
Representative Projects
- Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows.
- Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.
- Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows.
- Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.
You May Be a Good Fit If You
- Are proficient in Python and async/concurrent programming with frameworks like Trio
- Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)
- Have industry experience in machine learning research
- Can balance research exploration with engineering implementation
- Enjoy pair programming (we love to pair!)
- Care about code quality, testing, and performance
- Have strong systems design and communication skills
- Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
Strong Candidates May Have
- Familiarity with LLM architectures and training methodologies
- Experience with reinforcement learning techniques and environments
- Experience with virtualization and sandboxed code execution environments
- Experience with Kubernetes
- Experience with distributed systems or high-performance computing
- Experience with Rust and/or C++
Strong Candidates Need Not Have
- Formal certifications or education credentials
- Academic research experience or publication history
Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary
$500,000—$850,000 USD
Logistics
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us.
To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
How We're Different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.
Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic
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 Teams
Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.5 and Opus 4.5. Our work spans several key areas:
- Developing systems that enable models to use computers effectively
- Advancing code generation through reinforcement learning
- Pioneering fundamental RL research for large language models
- Building scalable RL infrastructure and training methodologies
- Enhancing model reasoning capabilities
We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.
About The Role
As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.
Representative Projects
- Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows.
- Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.
- Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows.
- Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.
You May Be a Good Fit If You
- Are proficient in Python and async/concurrent programming with frameworks like Trio
- Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)
- Have industry experience in machine learning research
- Can balance research exploration with engineering implementation
- Enjoy pair programming (we love to pair!)
- Care about code quality, testing, and performance
- Have strong systems design and communication skills
- Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
Strong Candidates May Have
- Familiarity with LLM architectures and training methodologies
- Experience with reinforcement learning techniques and environments
- Experience with virtualization and sandboxed code execution environments
- Experience with Kubernetes
- Experience with distributed systems or high-performance computing
- Experience with Rust and/or C++
Strong Candidates Need Not Have
- Formal certifications or education credentials
- Academic research experience or publication history
Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary
$500,000—$850,000 USD
Logistics
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us.
To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
How We're Different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.
Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
See all 24+ AI ML Engineer at Anthropic jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new AI ML Engineer at Anthropic roles.
Get Access To All JobsTips for Finding AI ML Engineer Jobs at Anthropic Jobs
Align your portfolio to safety research
Anthropic's AI ML Engineer roles sit at the intersection of capability and alignment. Before applying, reframe your experience around evaluation frameworks, RLHF pipelines, or interpretability work rather than generic ML engineering projects.
Target roles that map to specialty occupation criteria
USCIS requires H-1B positions to qualify as specialty occupations. Anthropic's ML Engineer postings typically require a relevant degree in computer science, statistics, or a related field, so ensure your credentials reflect that direct alignment in your application materials.
Prepare for a technical loop that mirrors research interviews
Anthropic's interview process for this function often includes systems design, ML theory, and coding rounds alongside research discussion. Preparing published papers or technical write-ups on model training or evaluation gives you concrete material to reference.
Request a filing timeline during offer negotiation
If you're on OPT, the H-1B cap registration window opens in March and USCIS typically begins accepting petitions October 1. Confirm during the offer stage that Anthropic will file within your remaining work authorization window so there's no gap in status.
Use Migrate Mate to find open roles before they fill
AI ML Engineer positions at Anthropic move quickly. Use Migrate Mate to filter for Anthropic roles that match your visa type so you're applying to openings that are actively accepting sponsored candidates, not listings that have already closed.
AI ML Engineer at Anthropic jobs are hiring across the US. Find yours.
Find AI ML Engineer at Anthropic JobsFrequently Asked Questions
Does Anthropic sponsor H-1B visas for AI ML Engineers?
Yes, Anthropic sponsors H-1B visas for AI ML Engineer roles. The company participates in the annual H-1B cap registration process, so if you're subject to the cap, your employer needs to register during the March window and petitions become effective October 1. Anthropic also sponsors H-1B1 and E-3 visas, which have separate annual allocations and do not require lottery selection.
How do I apply for AI ML Engineer jobs at Anthropic?
Applications go through Anthropic's careers page. Tailor your resume to reflect the specific research or engineering focus of the role, whether that's model training, reinforcement learning from human feedback, or safety evaluation. You can also browse current openings filtered by visa type on Migrate Mate, which surfaces Anthropic roles that are open to sponsored candidates.
Which visa types are commonly used for AI ML Engineer roles at Anthropic?
Anthropic sponsors H-1B, H-1B1, and E-3 visas for this function. H-1B is the most widely used and applies to most nationalities. H-1B1 is available exclusively to Chilean and Singaporean nationals and has a dedicated annual cap. E-3 applies only to Australian citizens and carries its own separate allocation. All three require the role to qualify as a specialty occupation under USCIS definitions.
What qualifications does Anthropic expect for AI ML Engineer roles?
Most postings require a bachelor's degree or higher in computer science, mathematics, or a closely related field, though research-oriented roles often prefer candidates with graduate degrees or published work. Practical experience with large-scale model training, distributed systems, or ML evaluation frameworks is consistently emphasized. Anthropic's alignment focus means experience with RLHF, interpretability, or safety-adjacent research is a meaningful differentiator.
How long does the visa sponsorship process take when joining Anthropic as an AI ML Engineer?
For H-1B transfers from another employer, USCIS generally allows you to begin work upon filing, without waiting for approval. For new H-1B petitions subject to the cap, you need to be selected in the March lottery before the October 1 start date. E-3 and H-1B1 visas are typically processed at a U.S. consulate, with appointment wait times varying by location. Premium processing through USCIS reduces adjudication to 15 business days for eligible petition types.
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