Staff Machine Learning Engineer

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.

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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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 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.

• 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.

By day 30, you have reviewed our agents, evals and training pipeline and written down the three biggest gaps. By day 90, you have shipped a post-training or evaluation improvement that moved a production metric, and published the plan the team follows for the next two quarters.

• 🚀 Mission-Driven, World-Class Team — Join an exceptional group of professionals aligned around a meaningful mission and committed to making an impact

• 📈 Opportunities for Growth — Strengthen your expertise through collaboration with experienced, high-performing leaders across the organization

• 🏢 Flexible Hybrid Work Environment — We're remote-first, with meaningful office presence in San Francisco and New York. R&D roles follow a hybrid model, with two days per week in our San Francisco office

• 🏥 Healthcare Coverage — Full medical, dental, and vision insurance for you and participation for your family

• 💰 401(k) with Company Match — Plenful matches 50% of your first 3% contributed

• 📊 Equity — Every full-time employee shares in our success

• 🌴 Unlimited PTO — Take the time you need, when you need it

• 🍽️ Daily Lunch Stipend — $100/week to cover your midday meals

• 💪 Wellness Stipend — $100/month to support your health and well-being

• 🚇 Commuter Benefits — $100/month for SF and NYC-based employees

• 👶 Parental Leave — Paid leave to support growing families