Staff+ Software Engineer, Safeguards Evals

Anthropic Limited; New York (NY); Hybrid; USD 320,000 - 485,000; Full time

Benefits offered by this job

Competitive compensation

Equity donation matching

Generous vacation & parental leave

Flexible working hours

Office space

Job summary

Anthropic is seeking an experienced Evaluations Research Scientist to design experiments, build evaluation datasets, and ship tooling that gates model training and releases. You will work at the intersection of ML research and engineering, advancing measurement of model safety and behavior in real-world scenarios.

You will contribute to datasets representing real harm, develop agentic evaluation harnesses, and collaborate with Policy and Enforcement to translate observed patterns into measurable

Qualifications

• Experience building and maintaining data pipelines.
• Experience working with LLMs and understanding their capabilities and failure modes.
• Strong data analysis skills and ability to draw reliable insights from large datasets.
• Ability to move fluidly between research prototyping and production-quality code.
• Ability to translate ambiguous problems into concrete, testable experiments.
• Care about AI safety and want real impact.

Responsibilities

• Evaluation research and methodology: design and run experiments to improve evaluation quality and coverage.
• Agentic investigation evals: build and own evaluation harness and measure end-to-end performance.
• Datasets grounded in real harm: construct high-quality eval datasets across harm areas.
• Productionization and tooling: ship research into evaluation and release pipelines and build usable tooling.

Skills

Data pipelines

LLMs

Data analysis

Prototyping to production

Experiment design

AI safety

Education

Bachelor’s degree

Tools

Job description

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

How do we know whether a model is safe - and how do we know whether the systems we built to catch misuse actually catch it?

Anthropic answers both questions with evaluations. We measure model behavior across misuse, prompt injection, and user well-being to inform training and deployment decisions. We also use AI to investigate potential misuse of Claude, analyzing real-world traffic to surface bad actors and emerging threats that drive enforcement actions. Neither is worth much unless the evaluations behind them are representative, robust, and trustworthy.

This role builds the methods and infrastructure that make them so. Sitting at the intersection of applied ML research and engineering, you'll design experiments to improve how we evaluate both model and behavior and the agentic systems that govern it, build datasets that represent real abuse rather than clean approximations of it, and ship those methods into the pipelines that gate model training, agent changes, and launch decisions.

Responsibilities

• Evaluation research and methodology. Design and run experiments to improve evaluation quality — developing methods to generate representative test data, simulate realistic user behavior, and validate grading accuracy. Research how different factors (multi-turn conversations, tools, long context, user diversity) impact model safety behavior. Analyze evaluation coverage to identify measurement gaps, and evolve evals so they remain unsaturated and high-signal as model and agent capabilities advance.
• Agentic investigation evals. Build and own the evaluation harness for an agentic investigation system — defining metrics, test cases, and grading approaches for a complex, long-horizon agent. Measure agent performance end-to-end (detection precision and recall, investigation quality, robustness) and drive hill-climbing on the hardest harm areas. Construct RL environments to improve Claude's safety investigation capabilities.
• Datasets grounded in real harm. Construct high-quality eval datasets representing real-world misuse across harm areas such as cyber attacks, bio weapons, and influence operations, drawing from both real traffic patterns and synthetic generation. Collaborate with Policy and Enforcement to translate observed harm patterns into measurable evaluations.
• Productionization and tooling. Ship successful research into evaluation, regression, and release pipelines that run during model training, on every agent change, prompt update, and underlying model upgrade, and beyond launch. Build tooling that enables policy experts to author, run, and iterate on evaluations without engineering support. Surface findings to research and training teams to drive upstream model improvements.

Minimum qualifications

• Experience building and maintaining data pipelines
• Experience working with LLMs and a working understanding of their capabilities and failure modes — especially agentic systems with tool use and multi-step reasoning
• Strong data analysis skills — you can draw reliable insights from large datasets
• Ability to move fluidly between research prototyping and production-quality code
• Ability to translate ambiguous problems into concrete, testable experiments
• Care deeply about AI safety and want your work to have real impact

Preferred qualifications

• 8+ years of industry software engineering or ML engineering experience
• Expertise in building or contributing to LLM or agent evaluation frameworks, benchmarks, or automated grading systems
• Extensive experience in trust and safety, content moderation, or abuse detection systems
• Experience in red teaming, adversarial testing, or jailbreak research on AI systems
• Experience with synthetic data generation or data augmentation
• Experience with distributed systems or large-scale data processing
• Experience with prompt engineering or building LLM-powered applications

Annual Salary

$320,000—$485,000 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.

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.

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.

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.

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.