Evals Infrastructure Tech Lead / Manager

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.

Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We're looking for an experienced tech lead to join our Evals Infrastructure team, building the systems that let us measure what our models can actually do. Evaluation is how we know whether a model is safe to ship — you'd own the infrastructure that makes those measurements fast, reliable, and trustworthy at scale. In this role you'll work at the intersection of inference, research and infrastructure engineering: managing the large scale distributed systems that orchestrate evals for our frontier models, building and scaling the harnesses researchers use to design and run evals, making results reproducible and interpretable, and ensuring eval signal is available where decisions get made. Your work directly shapes what we build and what we don't.

You may be a good fit if you

Strong candidates may have

Sample Projects

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.

• Lead the team building the distributed systems that schedule, orchestrate, and execute evals for our frontier model training
• Own eval throughput and cost: compute allocation across suites, queueing against constrained accelerator pools, caching and reuse of eval work
• Build and scale the harnesses researchers use to define, run, and iterate on evals
• Make eval results trustworthy — determinism, reproducibility, and honest uncertainty quantification on reported metrics
• Ensure eval signal reaches the dashboards and reviews where launch decisions actually get made
• Contribute directly as an engineer while managing and growing the team, prioritizing its work, and coaching your reports
• Have led technical projects end-to-end on large-scale distributed systems, and have 1+ years managing engineers (or tech-lead-with-reports experience)
• Are strong in Python and Rust
• Have built high-throughput, fault-tolerant systems on cloud or on-prem accelerator fleets
• Care about measurement quality, not just pipeline uptime — you'd notice if a metric moved for the wrong reason
• Communicate well with researchers and can translate research needs into infrastructure
• Are deeply interested in the transformative effects of advanced AI and committed to safe development
• Worked on LLM inference or training infrastructure
• Experience with eval or benchmarking systems, especially agentic evals requiring sandboxed execution
• Working statistical literacy — variance, confidence intervals, sample-size sufficiency for noisy metrics
• Experience with observability and regression detection over time-series metrics
• Rebuilding the eval orchestration layer to cut wall-clock time on the pre-train eval suite
• Designing compute allocation and scheduling so eval suites fit inside a fixed fraction of a production run's chip-hours
• Adding rigorous uncertainty estimates to top-line dashboard metrics so checkpoint-to-checkpoint comparisons are actually decision-grade
• Building sandboxed execution infrastructure for agentic evals

$500,000—$850,000 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.