Evals Infrastructure Tech Lead / Manager

About the role

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.

Responsibilities

• 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

Requirements

• Have led technical projects end-to-end on large-scale distributed systems, and have 1+ years managing engineers (or tech-lead-with-reports experience)
• Strong in Python and Rust
• Build 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
• Deeply interested in the transformative effects of advanced AI and committed to safe development

Qualifications

• 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

Skills

• Strong leadership and project management skills
• Experience with distributed systems and cloud infrastructure
• Ability to communicate complex technical concepts to non-technical stakeholders
• Experience with statistical analysis and machine learning

Benefits

• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Lovely office space for collaboration

Pay

The annual compensation range for this role is $500,000—$850,000 USD.

Schedule

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.

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.

Guidance on Candidates' AI Usage

Learn about our policy for using AI in our application process.