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Research Engineer, Frontier Red Team (RSP Evaluations)

San Francisco, CA | Seattle, WA

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 the team behind the Responsible Scaling Policy (RSP) Evaluations. The easiest way to understand what we do is to read the RSP sections of the Claude 3.7 system card and Claude 4 system card. We are the engineers who build automated systems to test whether frontier AI models are safe to release. Our evaluations determine if models have crossed critical capability thresholds in domains like autonomous replication, cybersecurity, and biological and chemical research.

This is a research engineering role where you'll build sophisticated evaluation infrastructure while thinking creatively about how to probe model capabilities. You'll work with our existing distributed systems to create automated pipelines that can run thousands of evaluation variants.You'll also need the curiosity and adversarial mindset to design tests that reveal what models can really do when pushed to their limits.

Responsibilities

  • Design and implement automated evaluation systems using our distributed infrastructure
  • Build sophisticated prompting pipelines that systematically probe model capabilities
  • Create tools that allow domain experts to quickly develop and deploy new evaluations
  • Develop evaluation strategies that think adversarially about model capabilities
  • Write production-quality Python code for high-throughput evaluation systems
  • Contribute to capability reports that directly inform model release decisions

You may be a good fit if you

  • Are resourceful, dependable, and care about safety
  • Think outside-the-box and challenge assumptions with curiosity
  • Are relentlessly motivated to find gaps/holes and have a hacker mindset
  • Have strong software engineering skills with extensive Python experience
  • Are comfortable working with async programming and distributed task systems
  • Write sophisticated prompts and understand how to systematically test models
  • Think creatively about edge cases and potential failure modes
  • Have experience with testing frameworks and automation

Strong candidates may also have

  • Have a hardened your grit in startups or high-stakes situations, and you know you won't drop the ball
  • Have experience evaluating dangerous capabilities, have designed or implemented evaluations, have used evaluation frameworks like Inspect or Promptfoo
  • Have experience building developer tools or testing infrastructure
  • Have a side interest in security research, adversarial testing, or red teaming

What makes this role unique

  • High-impact engineering: Your code determines whether models with unprecedented capabilities get released
  • Technical breadth: Work across distributed systems, evaluation frameworks, and adversarial testing
  • Creative problem-solving: Design evaluations for capabilities that have never existed before
  • Rapid iteration: Ship new evaluation systems as models evolve, requiring both speed and rigor
  • Cross-functional collaboration: Partner with world-class domain experts across multiple fields

Representative Projects

  • Built evaluation harness to maximally elicit Claude's capability in a specific domain, by connecting Claude to novel tools
  • Built reliable containerized environments to run evaluations on agentic Claude's, scaling it up to 1000s of concurrent evals.
  • Built automated analysis system that allows to reduce our evaluation time from days to minutes
  • Implemented monitoring infrastructure that caught capability jumps during model training
  • Wrote sophisticated prompting infrastructure to explore model behavior, for example to automatically red-team model behavior

Candidates need not have

  • Previous AI/ML research experience
  • Domain expertise in specific risk areas

Deadline to apply: None. Applications will be reviewed on a rolling basis.

The expected salary range for this position is:

Annual Salary:

$280,000 - $425,000 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.

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.

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