• You'll build a comprehensive system that runs fast, is easy to use, and supports quickly building new evals: • Speed: You’ll build a lightning-fast basic evals infrastructure that schedules tasks to introduce practically no latency; and then you’ll figure out clever ways to solve the fundamental sources of latency (building a version of Elicit, running it on a query, and evaluating it using LMs) • Interfaces: ML engineers need evals to kick off automatically on relevant commits, with results they can see at a glance and drill into. Product managers need dashboards showing performance over time and what's going wrong in production. • Architecture: Your code must be well-architected so other team members and ML engineers can understand and build on it. An engineer starting on a new feature should be able to quickly add examples and run an eval. • We need to evaluate how well Elicit actually helps with decision-making in pharma, not just measure what's easy to measure. This requires encoding real knowledge about how pharma customers make decisions (for example, choosing appropriate gold standards). • You'll provide appropriate statistical tests and confidence intervals so we can trust our results. • In a typical month, expect to spend: • 60% working on the core eval platform • 15% working closely with the evals team to build and improve specific evals (e.g., an eval of our paper search within our systematic review flow) • 10% mentoring our evals engineering intern • The rest on learning how people interact with the eval system so you can make it work better for them, and understanding what our users want from Elicit so evals measure what matters