Machine Learning Engineer Jobs at Anthropic with Visa Sponsorship
Machine Learning Engineer jobs at Anthropic involve working at the frontier of AI safety research and large-scale model development. The company sponsors H-1B visa, H-1B1 visa, and E-3 visas for this function, reflecting a consistent commitment to hiring international talent for highly specialized technical roles.
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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 Machine Learning Engineer Jobs at Anthropic
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Engineer Jobs at Anthropic.
Get Access To All JobsTips for Finding Machine Learning Engineer Jobs at Anthropic
Align your research to Anthropic's safety focus
Anthropic prioritizes interpretability, alignment, and scalable oversight. Frame your resume and portfolio around work that intersects ML engineering with safety-relevant outcomes, not just model performance benchmarks or product delivery metrics.
Prepare for multi-stage technical evaluation
Anthropic's ML Engineer interviews typically include research discussions alongside systems and coding rounds. Have recent work documented clearly so you can speak to architectural decisions, failure modes, and tradeoffs under direct questioning.
Confirm your visa category with HR before signing
Anthropic sponsors H-1B, H-1B1 visa, and E-3 visas. Your nationality determines which path applies, and each has different filing timelines and employer obligations. Confirm which category Anthropic will file before accepting an offer to avoid surprises during onboarding.
Time your application around the H-1B cap
If you need H-1B sponsorship, USCIS registration opens in March for an October 1 start date. Anthropic's ML Engineering roles are cap-subject unless you're already on a valid H-1B, so a late offer can mean waiting an additional year to start.
Use Migrate Mate to find open roles by visa type
Anthropic posts ML Engineering positions across several specializations at once. Use Migrate Mate to filter open roles by the visa types Anthropic sponsors, so you're targeting positions where your immigration pathway is already confirmed.
Secure strong documentation for specialty occupation evidence
USCIS scrutinizes ML Engineer petitions when the role blends research and engineering. A detailed offer letter specifying degree requirements in a specific technical field and a well-defined job duties description strengthens the specialty occupation argument your employer will need to make.
Frequently Asked Questions
Does Anthropic sponsor H-1B visas for Machine Learning Engineers?
Yes, Anthropic sponsors H-1B visas for Machine Learning Engineers. The company has an active sponsorship track record for this function and files petitions on behalf of international hires in technical roles. If you require H-1B sponsorship, confirm the timeline with your recruiter early, as cap-subject petitions must be filed months before your intended start date.
How do I apply for Machine Learning Engineer jobs at Anthropic?
Apply directly through Anthropic's careers page or find open roles filtered by visa sponsorship type on Migrate Mate. Anthropic's ML Engineer hiring process typically includes a recruiter screen, technical assessments covering systems design and ML fundamentals, and research-focused interviews. Tailoring your application to Anthropic's published work on AI safety and interpretability improves your chances of advancing.
Which visa types does Anthropic commonly sponsor for Machine Learning Engineers?
Anthropic sponsors H-1B, H-1B1 visa, and E-3 visas for Machine Learning Engineers. H-1B is available to most nationalities and is the most common path. H-1B1 applies to Chilean and Singaporean nationals, while E-3 is exclusively for Australian citizens. Each category has distinct filing procedures, and your nationality determines which option applies.
What qualifications does Anthropic expect from Machine Learning Engineer candidates?
Anthropic typically expects a graduate degree or equivalent research experience in machine learning, computer science, or a related field. Practical experience with large-scale model training, distributed systems, or ML infrastructure is valued. For visa purposes, your degree field should align closely with the role's duties, as USCIS requires a direct relationship between your academic background and the position.
How do I plan my timeline if I need visa sponsorship to work at Anthropic?
The timeline depends on your visa category. E-3 and H-1B1 visa petitions can be filed year-round with relatively short processing windows. Cap-subject H-1B petitions require USCIS lottery registration in March, with employment starting no earlier than October 1. If you receive an offer outside that window, discuss whether Anthropic can support a deferred start or premium processing to minimize the gap.