ML Engineer Jobs at Anthropic with Visa Sponsorship
ML Engineer jobs at Anthropic involve frontier AI safety research, model training, and interpretability work, with the company actively sponsoring work visas for qualified candidates. If you're on an H-1B visa, H-1B1 visa, or E-3 visa, Anthropic has a track record of supporting international talent through the sponsorship process.
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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's Safeguards team builds the systems that detect and mitigate misuse of our AI models, from individual policy violations to sophisticated, coordinated attacks. A growing part of that work depends on lightweight detection methods trained on model internals, which let us identify harmful behavior cheaply and at scale. This work feeds directly into Anthropic's Responsible Scaling Policy commitments. We're looking for an engineer to own the infrastructure behind that research. This is the tooling our researchers rely on to run experiments, train detection methods, and select detections for launch. It sits between research and production: researchers depend on it for fast iteration, and our detection systems depend on it for reliable, correct results as our models continue to change. Running machine learning workloads at our scale often requires solving novel systems problems. You'll identify those problems and build the abstractions, pipelines, and tooling that keep the research loop fast as requirements shift underneath you. Strong candidates will have a track record of solving large-scale systems and data problems and will be excited to grow deep machine learning expertise alongside it.
Key Responsibilities
- Build and scale the infrastructure and data pipelines behind Safeguards machine learning research
- Own the training, evaluation, and scoring workflows researchers use, with a focus on cutting the time between an idea and a result
- Design tooling and interfaces, including libraries and command line tools, that researchers can use directly without needing to understand the systems underneath
- Build correctness and sanity checking into the stack, so results stay trustworthy as models and workloads evolve
- Take the highest-value research workflows from experiments to reliable, production-grade jobs
- Improve the throughput, cost, and reliability of large-scale inference and scoring workloads
- Partner closely with researchers and engineers across Safeguards to understand their workflows, anticipate how their needs will change, and design for that ahead of time
Minimum Qualifications
- Strong software engineering fundamentals and hands-on coding ability, with proficiency in Python
- Experience building and operating data-intensive or distributed systems in production
- Experience building tooling or infrastructure that other engineers or researchers use as a dependency
- Comfort working across the research-to-deployment pipeline, from exploratory experiments to production systems
- Ability to debug performance and correctness problems across an unfamiliar stack
- Strong written and verbal communication skills, and a collaborative approach to technical decisions
Preferred Qualifications
- Experience with high-performance, large-scale machine learning systems
- Familiarity with language modeling and transformers, including working with model internals
- Experience with machine learning framework internals, GPU or accelerator programming, or inference optimization
- Experience building experiment tracking, caching layers, or evaluation harnesses for research teams
- Experience with probes, interpretability, or classifier development
- Interest in the misuse risks of AI systems and a desire to work on mitigating them
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—$500,000 USD
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. 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
Anthropic's Safeguards team builds the systems that detect and mitigate misuse of our AI models, from individual policy violations to sophisticated, coordinated attacks. A growing part of that work depends on lightweight detection methods trained on model internals, which let us identify harmful behavior cheaply and at scale. This work feeds directly into Anthropic's Responsible Scaling Policy commitments. We're looking for an engineer to own the infrastructure behind that research. This is the tooling our researchers rely on to run experiments, train detection methods, and select detections for launch. It sits between research and production: researchers depend on it for fast iteration, and our detection systems depend on it for reliable, correct results as our models continue to change. Running machine learning workloads at our scale often requires solving novel systems problems. You'll identify those problems and build the abstractions, pipelines, and tooling that keep the research loop fast as requirements shift underneath you. Strong candidates will have a track record of solving large-scale systems and data problems and will be excited to grow deep machine learning expertise alongside it.
Key Responsibilities
- Build and scale the infrastructure and data pipelines behind Safeguards machine learning research
- Own the training, evaluation, and scoring workflows researchers use, with a focus on cutting the time between an idea and a result
- Design tooling and interfaces, including libraries and command line tools, that researchers can use directly without needing to understand the systems underneath
- Build correctness and sanity checking into the stack, so results stay trustworthy as models and workloads evolve
- Take the highest-value research workflows from experiments to reliable, production-grade jobs
- Improve the throughput, cost, and reliability of large-scale inference and scoring workloads
- Partner closely with researchers and engineers across Safeguards to understand their workflows, anticipate how their needs will change, and design for that ahead of time
Minimum Qualifications
- Strong software engineering fundamentals and hands-on coding ability, with proficiency in Python
- Experience building and operating data-intensive or distributed systems in production
- Experience building tooling or infrastructure that other engineers or researchers use as a dependency
- Comfort working across the research-to-deployment pipeline, from exploratory experiments to production systems
- Ability to debug performance and correctness problems across an unfamiliar stack
- Strong written and verbal communication skills, and a collaborative approach to technical decisions
Preferred Qualifications
- Experience with high-performance, large-scale machine learning systems
- Familiarity with language modeling and transformers, including working with model internals
- Experience with machine learning framework internals, GPU or accelerator programming, or inference optimization
- Experience building experiment tracking, caching layers, or evaluation harnesses for research teams
- Experience with probes, interpretability, or classifier development
- Interest in the misuse risks of AI systems and a desire to work on mitigating them
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—$500,000 USD
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. 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 ML Engineer Jobs at Anthropic
Sign up for free to unlock all listings, filter by visa type, and get alerts for new ML Engineer Jobs at Anthropic.
Get Access To All JobsTips for Finding ML Engineer Jobs at Anthropic
Align your research background to Anthropic's focus
Anthropic prioritizes candidates with experience in large-scale model training, RLHF, or interpretability research. Frame your CV around safety-adjacent ML work, not just general engineering, so your application maps directly to their published research agenda.
Clarify your visa category before applying
Anthropic sponsors H-1B, H-1B1 visa, and E-3 visas. Knowing which category applies to you lets you ask informed questions during the offer stage and avoids delays when the recruiting team routes your file to their immigration counsel.
Target roles outside the H-1B lottery window
If you're mid-OPT or between jobs, apply when Anthropic posts roles between May and September. Cap-subject H-1B petitions filed for October 1 start dates mean offers extended outside lottery season can move faster for E-3 and H-1B1 visa holders who aren't subject to the annual cap.
Prepare your specialty occupation documentation early
USCIS scrutinizes ML Engineer petitions heavily. Gather transcripts, publications, and employer letters that tie your specific degree field to the role's technical requirements before your offer letter arrives, so your attorney can file a strong I-129 without delays.
Use Migrate Mate to find open ML Engineer roles at Anthropic
Anthropic posts ML Engineer openings across research, infrastructure, and product teams. Browse current listings filtered by visa sponsorship on Migrate Mate so you're applying to active roles that explicitly support international candidates, not postings with expired headcount.
Negotiate start dates around premium processing availability
If your visa petition needs to clear quickly, ask your Anthropic recruiter whether the company will use USCIS premium processing on your I-129. This reduces the standard adjudication window from several months to roughly 15 business days and protects your intended start date.
Frequently Asked Questions
Does Anthropic sponsor H-1B visas for ML Engineers?
Yes, Anthropic sponsors H-1B visas for ML Engineer roles. The company also sponsors H-1B1 visas for Chilean and Singaporean nationals and E-3 visas for Australian citizens, giving it more flexibility than employers that support only the standard H-1B. Sponsorship is handled through their in-house legal team and outside immigration counsel after an offer is extended.
Which visa types are commonly used for ML Engineer roles at Anthropic?
H-1B is the most common pathway for ML Engineers at Anthropic, but Australian and Singaporean nationals often use the E-3 and H-1B1 visa respectively, since those categories aren't subject to the annual lottery. For candidates already in the U.S. on F-1 OPT or STEM OPT, Anthropic can file a change of status to H-1B or an applicable treaty visa without requiring you to leave the country.
What qualifications and experience does Anthropic expect for ML Engineer roles?
Anthropic's ML Engineer postings consistently require a graduate degree or equivalent research experience in machine learning, computer science, or a related field. Hands-on work with large language models, distributed training infrastructure, or safety-relevant techniques like RLHF and interpretability methods carries significant weight. Published research or open-source contributions to ML frameworks are strong differentiators in a competitive applicant pool.
How do I apply for ML Engineer jobs at Anthropic?
Applications go through Anthropic's careers page, where roles are categorized by team, including research, alignment, and product infrastructure. You can find current ML Engineer openings filtered by visa sponsorship on Migrate Mate, which makes it straightforward to identify active roles that explicitly support international candidates. The process typically involves a recruiter screen, technical assessments, and a multi-round interview before an offer.
How do I plan my timeline if Anthropic is sponsoring my H-1B petition?
If you're subject to the H-1B cap, your petition must be filed between April 1 and the lottery registration window in March. Offers for October 1 start dates are typically extended in late winter. If you're on STEM OPT, your 24-month extension should cover the gap. H-1B1 visa and E-3 holders can file year-round without lottery constraints, which makes scheduling significantly more predictable.