Research Engineer, Post-Training Model Evaluations

Anthropic's production models undergo sophisticated post-training processes to enhance capabilities, alignment, and safety. The team's mission is to have the best observability into production post-training runs, shaping what gets measured, keeping the signal trustworthy and timely, and investigating surprising results to inform post-training and release decisions. In this role, you will lead the science of how we evaluate production training runs, working out which measurements tell us something real about the model, noticing when they stop doing so, and finding what should be measured but isn't. You'll partner with research teams across every RL domain, bringing their priorities into what we measure and setting the standard for what makes an eval trustworthy across post-training. You'll also be hands-on with the eval fleet day to day.

• Steer the eval strategy for production Claude models: study and optimize the eval mix so it gives the most accurate and complete assessment of model quality.
• Run and monitor the eval fleet live to understand how each Claude model is developing as it trains.
• Advise teams across post-training on eval methodology, and help eval authors bring new evals up to the bar for production.
• Investigate regressions in production runs and inform training interventions when appropriate.
• Build the dashboards, alerts, and reports that researchers and leadership use to track model quality.

• Have designed, run, and analyzed evaluations for ML models at scale.
• Care deeply about measurement quality, thinking twice before trusting a number.
• Can turn ambiguous results into clear recommendations, and are comfortable influencing direction across teams.
• Have strong Python skills and are comfortable working with production systems.
• Maintain clarity and rigor when debugging complex, time-sensitive issues.
• Thrive in controlled chaos and are energized, rather than overwhelmed, when juggling multiple urgent priorities during a live training run.
• Care about the societal impacts of your work and about shipping frontier models responsibly.

• Hands-on experience post-training large language models.
• Research experience in ML evaluation or benchmarking.
• Background in statistics and experimental design.
• Experience developing robust evaluation metrics for ML systems.

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.