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
We are looking for ML Engineers and Research Engineers to help detect and mitigate misuse of our AI systems. As a member of the Safeguards ML team, you will build systems that identify harmful use—from individual policy violations to sophisticated, coordinated attacks—and develop defenses that keep our products safe as capabilities advance. You will also work on systems that protect user wellbeing and ensure our models behave appropriately across a wide range of contexts. This work feeds directly into Anthropic's Responsible Scaling Policy commitments.
Responsibilities
- Develop classifiers to detect misuse and anomalous behavior at scale. This includes developing synthetic data pipelines for training classifiers and methods to automatically source representative evaluations to iterate on
- Build systems to monitor for harms that span multiple exchanges, such as coordinated cyber attacks and influence operations, and develop new methods for aggregating and analyzing signals across contexts
- Evaluate and improve the safety of agentic products—developing both threat models and environments to test for agentic risks, and developing and deploying mitigations for prompt injection attacks
- Conduct research on automated red-teaming, adversarial robustness, and other research that helps test for or find misuse
You may be a good fit if you
- Have 4+ years of experience in ML engineering, research engineering, or applied research, in academia or industry
- Have proficiency in Python and experience building ML systems
- Are comfortable working across the research-to-deployment pipeline, from exploratory experiments to production systems
- Are worried about misuse risks of AI systems, and want to work to mitigate them
- Have strong communication skills and ability to explain complex technical concepts to non-technical stakeholders
Strong Candidates May Also Have Experience With
- Language modeling and transformers
- Building classifiers, anomaly detection systems, or behavioral ML
- Adversarial machine learning or red-teaming
- Interpretability or probes
- Reinforcement learning
- High-performance, large-scale ML systems
Annual Salary
$350,000—$500,000 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 looking for ML Engineers and Research Engineers to help detect and mitigate misuse of our AI systems. As a member of the Safeguards ML team, you will build systems that identify harmful use—from individual policy violations to sophisticated, coordinated attacks—and develop defenses that keep our products safe as capabilities advance. You will also work on systems that protect user wellbeing and ensure our models behave appropriately across a wide range of contexts. This work feeds directly into Anthropic's Responsible Scaling Policy commitments.
Responsibilities
- Develop classifiers to detect misuse and anomalous behavior at scale. This includes developing synthetic data pipelines for training classifiers and methods to automatically source representative evaluations to iterate on
- Build systems to monitor for harms that span multiple exchanges, such as coordinated cyber attacks and influence operations, and develop new methods for aggregating and analyzing signals across contexts
- Evaluate and improve the safety of agentic products—developing both threat models and environments to test for agentic risks, and developing and deploying mitigations for prompt injection attacks
- Conduct research on automated red-teaming, adversarial robustness, and other research that helps test for or find misuse
You may be a good fit if you
- Have 4+ years of experience in ML engineering, research engineering, or applied research, in academia or industry
- Have proficiency in Python and experience building ML systems
- Are comfortable working across the research-to-deployment pipeline, from exploratory experiments to production systems
- Are worried about misuse risks of AI systems, and want to work to mitigate them
- Have strong communication skills and ability to explain complex technical concepts to non-technical stakeholders
Strong Candidates May Also Have Experience With
- Language modeling and transformers
- Building classifiers, anomaly detection systems, or behavioral ML
- Adversarial machine learning or red-teaming
- Interpretability or probes
- Reinforcement learning
- High-performance, large-scale ML systems
Annual Salary
$350,000—$500,000 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 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.