ApplySarthiSecurity Engineer / Research Engineer, RL Security
Anthropic
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Preparing for this interview
Interviews for security roles keep coming back to AWS, Python, CI/CD, GCP. Practise those questions before you sit with Anthropic.
Questions you are likely to be asked
- Why do you want to join Anthropic?
- What is your experience with Observability? Tell me one thing you learned the hard way.
- How do you keep secrets and access safe in your infrastructure?
- Walk me through how code gets from a commit to production where you work.
- Tell me about an outage you handled. What did you learn from it?
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Practise the Security Engineer / Research Engineer, RL Security at Anthropic interview free →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
Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. At frontier scale, an RL run is a large distributed system: training, sampling, and environment execution run concurrently across a large fleet of accelerators and hosts. As an engineer in RL Engineering, you'll work on keeping that system secure as it scales.
This is a hands-on engineering role. You'll work in the RL codebase and the infrastructure underneath it, alongside the engineers and researchers who build and use it. The system is large and changes as fast as the research does, so security work here has to keep up with that pace without costing performance or velocity.
We're hiring from two backgrounds: security engineers who want to work on large-scale ML systems, and ML systems or research engineers who want to work on security. Strong candidates have depth in one and real interest in the other.
Key responsibilities
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Design, build, and operate security controls for RL training systems and the infrastructure they run on
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Work across the stack (orchestration, environments, training, inference) wherever the work is needed
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Review designs and code across the RL stack, find security problems, and fix them
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Build shared libraries and tooling that other RL engineers and researchers build on
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Build the observability needed to understand what these systems are doing
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Work with researchers and performance engineers to make sure security changes preserve training correctness and performance
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Write clear design documents for what you build
Minimum qualifications
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Strong software engineering skills in Python and at least one systems language such as Rust, C++, or Go
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Experience designing, building, and operating large-scale distributed systems or security systems in production
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Strong understanding of security fundamentals
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Working knowledge of Linux, containers, and cloud infrastructure
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Experience debugging complex problems across many hosts and services
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Ability to reason about tradeoffs between security, performance, and reliability
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Strong written communication, including design documents
Preferred qualifications
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Experience running ML training or inference infrastructure at scale
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Experience with infrastructure security, such as isolation, access control, networking, or identity
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Experience with container orchestration such as Kubernetes, and with sandboxed or virtualized code execution at scale
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Experience building observability for large fleets
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Experience supporting research teams or other fast-moving internal users
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Familiarity with reinforcement learning or large language model training workloads
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.
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.
For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.
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.
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Listed on greenhouse · posted 2026-10-20. ApplySarthi collects openings and links to application pages; the role is advertised by Anthropic, not by us.