ML Software Engineer Jobs at Anthropic with Visa Sponsorship
Anthropic hires ML Software Engineers to work on foundational AI safety research and large-scale model development. The company sponsors H-1B, H-1B1, and E-3 visas for qualified candidates, making it a realistic target if you're on a work visa pathway and have strong ML research or engineering 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 27+ ML Software Engineer at Anthropic jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new ML Software Engineer at Anthropic roles.
Get Access To All JobsTips for Finding ML Software Engineer Jobs at Anthropic Jobs
Align your credentials with safety research
Anthropic's ML roles sit at the intersection of engineering and research. Framing your experience around model training, alignment techniques, or interpretability work strengthens your application well before you submit anything to USCIS.
Target roles by research area not job title
Anthropic posts ML Software Engineer roles across distinct teams: pretraining, RLHF, and inference. Applying to the team whose published research matches your background signals genuine fit, which matters when an employer is weighing visa sponsorship costs.
Track open roles using Migrate Mate
Anthropic's ML Software Engineer openings move quickly. Use Migrate Mate to filter for Anthropic roles by visa type so you're applying when positions are live, not after a requisition closes.
Prepare for a research-weighted technical process
Anthropic's ML interview loop includes systems design and ML theory rounds alongside coding. Having published work, open-source contributions, or documented experiments makes the sponsorship conversation easier because the technical bar is already cleared.
Account for LCA and I-129 timing in your start date
Your employer must file a certified Labor Condition Application with DOL before USCIS can process your H-1B petition. Build at least six to eight weeks into your expected start date to account for LCA certification and petition processing after you receive an offer.
ML Software Engineer at Anthropic jobs are hiring across the US. Find yours.
Find ML Software Engineer at Anthropic JobsFrequently Asked Questions
Does Anthropic sponsor H-1B visas for ML Software Engineers?
Yes, Anthropic sponsors H-1B visas for ML Software Engineers. The company also sponsors H-1B1 visas for eligible nationals from Chile and Singapore, and E-3 visas for Australian citizens. Sponsorship is handled through standard USCIS petition processes, and Anthropic has an established track record of filing across these categories for technical and research roles.
Which visa types are commonly used for ML Software Engineer roles at Anthropic?
ML Software Engineers at Anthropic most commonly work on H-1B visas, which require a specialty occupation and a bachelor's degree or higher in a related field. Australian citizens can pursue the E-3, which has no annual lottery and allows two-year renewable periods. Nationals from Chile and Singapore may qualify for the H-1B1. All three require a certified Labor Condition Application filed with DOL before USCIS processing begins.
What qualifications or experience does Anthropic expect for ML Software Engineer roles?
Anthropic typically looks for strong foundations in machine learning, deep learning systems, and large-scale model training. Research experience, whether through publications, graduate work, or significant open-source contributions, is weighted heavily. Proficiency in Python and experience with distributed training frameworks are standard expectations. Roles on alignment or interpretability teams often require familiarity with RLHF or mechanistic interpretability research.
How do I apply for ML Software Engineer jobs at Anthropic?
You can find and filter current ML Software Engineer openings at Anthropic by visa type on Migrate Mate, which lets you confirm sponsorship eligibility before you apply. Applications go through Anthropic's careers portal. The process typically includes a recruiter screen, a technical phone interview, and a multi-round loop covering ML systems, coding, and research discussions. Communicating your visa status early prevents delays at the offer stage.
How long does the visa sponsorship process take after receiving an offer from Anthropic?
After an offer, your employer must obtain a certified Labor Condition Application from DOL, which typically takes one to two weeks under standard processing. USCIS then processes the I-129 petition, which can take two to six months under regular processing or as little as two to three weeks with premium processing. For H-1B cap-subject cases, timing also depends on the annual lottery cycle, with petitions eligible for an October 1 start date each fiscal year.
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