Machine Learning Jobs at Anthropic with Visa Sponsorship
Anthropic hires Machine Learning researchers and engineers to advance the frontier of AI safety and large language model development. The company sponsors H-1B, H-1B1, and E-3 visas for this function, making it a realistic target for international candidates with strong ML credentials.
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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 Role
We are seeking an experienced Machine Learning Systems Engineer to join our Encodings and Tokenization team at Anthropic. This cross-functional role will be instrumental in developing and optimizing the encodings and tokenization systems used throughout our Finetuning workflows. As a bridge between our Pretraining and Finetuning teams, you'll build critical infrastructure that directly impacts how our models learn from and interpret data. Your work will be foundational to Anthropic's research progress, enabling more efficient and effective training of our AI systems while ensuring they remain reliable, interpretable, and steerable.
Responsibilities
- Design, develop, and maintain tokenization systems used across Pretraining and Finetuning workflows
- Optimize encoding techniques to improve model training efficiency and performance
- Collaborate closely with research teams to understand their evolving needs around data representation
- Build infrastructure that enables researchers to experiment with novel tokenization approaches
- Implement systems for monitoring and debugging tokenization-related issues in the model training pipeline
- Create robust testing frameworks to validate tokenization systems across diverse languages and data types
- Identify and address bottlenecks in data processing pipelines related to tokenization
- Document systems thoroughly and communicate technical decisions clearly to stakeholders across teams
You May Be a Good Fit If You
- Have significant software engineering experience with demonstrated machine learning expertise
- Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
- Can work independently while maintaining strong collaboration with cross-functional teams
- Are results-oriented, with a bias towards flexibility and impact
- Have experience with machine learning systems, data pipelines, or ML infrastructure
- Are proficient in Python and familiar with modern ML development practices
- Have strong analytical skills and can evaluate the impact of engineering changes on research outcomes
- Pick up slack, even if it goes outside your job description
- Enjoy pair programming (we love to pair!)
- Care about the societal impacts of your work and are committed to developing AI responsibly
Strong Candidates May Also Have Experience With
- Working with machine learning data processing pipelines
- Building or optimizing data encodings for ML applications
- Implementing or working with BPE, WordPiece, or other tokenization algorithms
- Performance optimization of ML data processing systems
- Multi-language tokenization challenges and solutions
- Research environments where engineering directly enables scientific progress
- Distributed systems and parallel computing for ML workflows
- Large language models or other transformer-based architectures (not required)
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
$320,000—$405,000 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 Role
We are seeking an experienced Machine Learning Systems Engineer to join our Encodings and Tokenization team at Anthropic. This cross-functional role will be instrumental in developing and optimizing the encodings and tokenization systems used throughout our Finetuning workflows. As a bridge between our Pretraining and Finetuning teams, you'll build critical infrastructure that directly impacts how our models learn from and interpret data. Your work will be foundational to Anthropic's research progress, enabling more efficient and effective training of our AI systems while ensuring they remain reliable, interpretable, and steerable.
Responsibilities
- Design, develop, and maintain tokenization systems used across Pretraining and Finetuning workflows
- Optimize encoding techniques to improve model training efficiency and performance
- Collaborate closely with research teams to understand their evolving needs around data representation
- Build infrastructure that enables researchers to experiment with novel tokenization approaches
- Implement systems for monitoring and debugging tokenization-related issues in the model training pipeline
- Create robust testing frameworks to validate tokenization systems across diverse languages and data types
- Identify and address bottlenecks in data processing pipelines related to tokenization
- Document systems thoroughly and communicate technical decisions clearly to stakeholders across teams
You May Be a Good Fit If You
- Have significant software engineering experience with demonstrated machine learning expertise
- Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
- Can work independently while maintaining strong collaboration with cross-functional teams
- Are results-oriented, with a bias towards flexibility and impact
- Have experience with machine learning systems, data pipelines, or ML infrastructure
- Are proficient in Python and familiar with modern ML development practices
- Have strong analytical skills and can evaluate the impact of engineering changes on research outcomes
- Pick up slack, even if it goes outside your job description
- Enjoy pair programming (we love to pair!)
- Care about the societal impacts of your work and are committed to developing AI responsibly
Strong Candidates May Also Have Experience With
- Working with machine learning data processing pipelines
- Building or optimizing data encodings for ML applications
- Implementing or working with BPE, WordPiece, or other tokenization algorithms
- Performance optimization of ML data processing systems
- Multi-language tokenization challenges and solutions
- Research environments where engineering directly enables scientific progress
- Distributed systems and parallel computing for ML workflows
- Large language models or other transformer-based architectures (not required)
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
$320,000—$405,000 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 28+ Machine Learning at Anthropic jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning at Anthropic roles.
Get Access To All JobsTips for Finding Machine Learning Jobs at Anthropic Jobs
Frame your research around safety-relevant ML
Anthropic evaluates ML candidates on alignment, interpretability, and scalable oversight, not just benchmark performance. Tailor your resume and portfolio to show work that connects model behavior to safety properties, even if your background is in a broader ML subfield.
Distinguish which visa category fits you
Anthropic sponsors H-1B, H-1B1, and E-3 visas. Australian citizens should pursue the E-3 directly, it bypasses the H-1B lottery entirely. Chilean and Singaporean citizens qualify for H-1B1. Knowing your category before applying helps you ask the right questions during the offer stage.
Build a publication or open-source record before applying
Anthropic's ML hiring skews toward candidates with verifiable research contributions. A paper on arXiv, a meaningful open-source model contribution, or a cited technical blog post gives recruiters a concrete signal that your work meets the depth the role requires.
Use Migrate Mate to target Anthropic ML openings that sponsor
Not every ML job posting at Anthropic signals sponsorship eligibility upfront. Use Migrate Mate to filter for Anthropic roles that have an active sponsorship track record for your visa type, so you're not applying blind.
Ask about LCA timing before signing your offer
Your employer must file a certified Labor Condition Application with DOL before USCIS can receive your H-1B or E-3 petition. If your start date is tight, confirm with your Anthropic recruiter that LCA filing will begin immediately after offer acceptance, delays here push back your entire timeline.
Prepare for technical depth in the interview loop
Anthropic's ML interview process typically includes a research discussion round alongside coding. Have a clear, rehearsed explanation of your most technically demanding project, interviewers probe the decisions behind your approach, not just whether you got results.
Machine Learning at Anthropic jobs are hiring across the US. Find yours.
Find Machine Learning at Anthropic JobsFrequently Asked Questions
Does Anthropic sponsor H-1B visas for Machine Learnings?
Yes, Anthropic sponsors H-1B visas for Machine Learning roles. The company has a consistent sponsorship track record for this function across research and engineering positions. If you're subject to the H-1B cap and lottery, timing your application cycle matters, confirm with your recruiter whether your role qualifies for cap-exempt filing through a research institution partnership.
How do I apply for Machine Learning jobs at Anthropic?
Applications go through Anthropic's careers page, where ML roles are listed under research and engineering. Most positions require a cover letter or research statement alongside your resume. You can also find and filter Anthropic ML roles that actively support visa sponsorship using Migrate Mate, which surfaces sponsorship-eligible openings specifically for international candidates.
Which visa types does Anthropic sponsor for Machine Learning roles?
Anthropic sponsors H-1B, H-1B1, and E-3 visas for Machine Learning positions. H-1B covers most nationalities and requires clearing the annual lottery unless the role qualifies as cap-exempt. H-1B1 is available to Chilean and Singaporean citizens. E-3 is available to Australian citizens only and does not require a lottery, making it faster to process.
What qualifications does Anthropic expect for Machine Learning roles?
Most ML roles at Anthropic expect a graduate degree in a quantitative field, computer science, statistics, or a related discipline, or equivalent demonstrated research experience. Interpretability, RLHF, and large-scale training are recurring technical areas across job postings. Strong candidates typically have peer-reviewed publications, significant open-source contributions, or industry research experience at a frontier AI lab.
How do I handle visa timing if I receive an offer from Anthropic?
Timing depends on your visa category. H-1B petitions are generally filed for an October 1 start date, so an offer arriving mid-year may mean waiting several months to begin. E-3 and H-1B1 petitions can be filed year-round with faster turnarounds, often four to eight weeks from LCA certification to USCIS approval. Clarify your expected start date with Anthropic's immigration team as early as the offer stage.
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