Staff 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.

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

• Set the AI quality strategy: automated graders, calibrated LLM judges, regression suites, human review, failure taxonomies and offline‑to‑online validation.

• Make eval results the basis for release decisions.

You build loops that improve themselves

• Design the systems that turn production failures and user feedback into better datasets, evals and models.

• Set the standard for dataset versioning, observability and MLOps so every team's work is reproducible and measurable.

You raise the bar for everyone

• Co‑own the 12‑month ML roadmap with engineering and product leaders, balancing near‑term wins against longer bets.

• Lead technical reviews, mentor Senior engineers, and help us hire.

• Build privacy in by design: minimum necessary data, de‑identified or synthetic data where it works, and HIPAA‑aware practices throughout.

What we're looking for

• 8+ years in ML or software engineering, including 3+ years leading LLM or agentic systems in production.

• Deep applied post‑training experience. You have run supervised fine‑tuning, preference optimization, distillation or RL end to end and shipped a model that moved a production metric.

• You have designed an evaluation strategy for LLM or agent systems and seen it drive real release decisions.

• You have architected production agent systems and can name what broke and what you changed. Experience with frameworks like LangGraph, RAG and vector search (for example Pinecone) is expected.

• Strong Python and PyTorch, and comfort with distributed training and serving (for example vLLM, FSDP or DeepSpeed).

• Real MLOps depth: Docker, Kubernetes, MLflow or equivalent, CI/CD, and experiment and dataset tracking.

• You influence without authority. Your design docs, reviews and mentoring make other engineers better.

You stay current, and others rely on it

• You use new models and coding agents the week they ship, and colleagues ask you whether a new thing is real.

• You turn papers and releases into experiments within days and report back with numbers.

• You have proof: open source, write-ups, talks, or evals other people use.

Nice to have

• Healthcare data experience: EHR, FHIR, claims, prior authorization.

• Publications or open‑source work in LLM evaluation, post‑training or agents.

• Experience interviewing and hiring ML engineers.

• Work in another regulated, high‑stakes domain.

What your first 90 days look like

By day 30, you have reviewed our ag