Senior Machine Learning Engineer

About the role

ABOUT PLENFUL

Plenful is on a mission to transform healthcare operations from the inside out. Fresh off our $50M Series B and backed by Notable Capital, Bessemer Venture Partners, TQ Ventures, Susa/Kivu Ventures, and other leading investors, we’re building the category-defining AI automation platform that healthcare teams rely on to operate smarter, faster, and more efficiently. Our technology empowers healthcare operators across hospital and health systems, pharmacies and payors to eliminate manual work, reduce administrative burden, and improve compliance, all while unlocking critical revenue to fund programs for their in-need patient populations.

Built by healthcare operators for healthcare operators, Plenful is driven by a deep understanding of the challenges facing today’s care teams. We’re passionate about equipping healthcare workers with world-class tools that deliver real, measurable impact, and we’re proud to serve 100+ leading health systems, pharmacies, and healthcare organizations across the country. If you’re excited to help shape the future of healthcare, we’d love to meet you. Apply now to join our growing team.

ABOUT THE ROLE

Healthcare's back office still runs on faxes, PDFs and phone calls. AI can take that work off people's plates, but only if it is accurate enough to trust with patient and financial data.

At Plenful, we build agents and models that do that work in production, with people in the loop wherever judgment matters. As a Senior Machine Learning Engineer, you own the pieces that make those systems reliable: the agents, the models behind them, and the data and evaluation loop that improves both every week.

WHAT YOU'LL DO

YOU SHIP AGENTS THAT DO REAL WORK

- Design, build and run production agents that use tools, retrieval and multi-step reasoning to finish healthcare workflows end to end.

- Build the orchestration, tracing and failure recovery that keep agents dependable when inputs are messy.

- Decide when a problem needs an agent, a model or a deterministic rule, and route low-confidence cases to a person.

YOU MAKE MODELS BETTER, NOT JUST CALL THEM

- Fine-tune and post-train LLMs and vision-language models on healthcare tasks, using methods like supervised fine-tuning, preference optimization and distillation.

- Choose between prompting, retrieval and fine-tuning for each problem, and prove the choice with data.

- Balance quality, latency and cost when you serve models.

YOU OWN THE LOOP THAT MAKES IT ALL IMPROVE

- Build and version datasets: labeled sets, golden sets and synthetic data drawn from production signal.

- Write evals that catch regressions before customers do: automated graders, LLM-as-judge calibrated against human labels, and regression suites that run in CI.

- Instrument production with tracing, drift detection and failure taxonomies, then feed what you learn back into training data.