Canvas Medical is the electronic medical records (EMR) and payments development platform for healthcare. We build modern, elegant front- and back-end tooling to enable new ways for developers and clinicians to collaborate to solve healthcare’s toughest challenges. Canvas is institutionally backed by some of the greatest technology investors in the world (funded notable health tech companies such as GoodRx, Oscar Health, and Hims & Hers Health). The Role We’re hiring an Applied AI Software Engineer to lead evaluations for agents in development and the post-deployment fleet of agents operating in Canvas to automate work for our customers. You will help develop agents in Canvas using state of the art foundation model inference and fine-tuning APIs along with our server-side SDK. The server-side SDK provides extensive tools and virtually all the context necessary for excellent agent performance. You’ll be responsible for designing and running rigorous evaluation experiments that measure performance, safety, and reliability across a wide variety of clinical, operational, and financial use cases. This role is ideal for someone with deep experience evaluating LLM-based agents at scale. You’ll create high-fidelity unit evals and end-to-end evaluations, define expert-determined ground truth outcomes, and manage iterations across model variants, prompts, tool use, and context window configurations. Your work will directly inform model selection, fine-tuning, and go/no-go decisions for AI features used in production settings.
You’ll collaborate with product, ML engineering, and clinical informatics teams to ensure that Canvas's AI agents are not only capable, but trustworthy and robust under real-world healthcare constraints. You will also work with technical product marketers and developer advocates to help our broader developer community and the broader market understand the uniquely differentiated value of agents in Canvas.