Research Engineer, Post-Training Model Evaluations

<div class="content-intro"><h2><strong>About Anthropic</strong></h2> <p>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.</p></div><h2>About the role&nbsp;</h2> <p>Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. Decisions in production training depend on knowing how the model is really behaving. Our team's mission is to have the best observability into production post-training runs at Anthropic: to be the clearest, most trusted view of model quality during training. We shape what gets measured, keep the signal trustworthy and timely, and get to the bottom of surprising results to inform post-training and release decisions.</p> <p>In this role you will lead the science of how we evaluate production training runs. You'll work out which measurements tell us something real about the model, notice when they stop doing so, and find what we should be measuring but aren'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, because the best questions about measurement come from watching real results come in during a live run.</p> <h2><strong>Responsibilities</strong></h2> <ul> <li>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</li> <li>Run and monitor the eval fleet live to understand how each Claude model is developing as it trains&nbsp;</li> <li>Advise teams across post-training on eval methodology, and help eval authors bring n