Machine Learning Engineer

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 Staff Machine Learning Engineer, you set the technical direction for how we build, train and measure AI across the company, and you stay hands-on while you do it. This is an individual contributor role with no direct reports and broad influence.

What you'll do

You set the direction for how our agents are built

• Define the architecture for production agents: tool use, retrieval, state and context management, routing, orchestration, tracing and failure recovery.

• Turn what works into patterns other engineers reuse.

• Make the calls on build versus buy for models and platform pieces.

You own our post-training strategy

• Decide where supervised fine-tuning, preference optimization, distillation and reinforcement learning pay off, and where they do not.

• Train and ship LLMs and vision-language models for healthcare tasks, and build the in-house capability to keep doing it.

• Prove every gain with evaluation that holds up in production.

You define what good looks like