Staff Machine Learning Engineer
Staff ML Engineer at Plenful sets the technical direction for production AI agents that automate healthcare back-office work, staying hands-on as an IC: owning LLM/VLM post-training, evaluation strategy, and MLOps. Core stack: Python, PyTorch, LangGraph, RAG/vector search, vLLM, Docker, Kubernetes, MLflow.
- Compensation
- Not disclosed
- Location
- San Francisco, CA
- Work setup
- Hybrid
- Seniority
- Staff
- Employment
- Full time
- Experience
- 3+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 8+ years in ML or software engineering
- 3+ years leading LLM or agentic systems in production
- Deep applied post-training experience: supervised fine-tuning, preference optimization, distillation or RL end to end, with a shipped model that moved a production metric
- Designed an evaluation strategy for LLM or agent systems that drove real release decisions
- Architected production agent systems; experience with frameworks like LangGraph, RAG and vector search (e.g. Pinecone)
- Strong Python and PyTorch; comfort with distributed training and serving (e.g. vLLM, FSDP, DeepSpeed)
- Real MLOps depth: Docker, Kubernetes, MLflow or equivalent, CI/CD, experiment and dataset tracking
- Influences without authority via design docs, reviews and mentoring
- Stays current: uses new models and coding agents as they ship, turns papers into experiments within days
- Public proof of expertise: open source, write-ups, talks, or evals others 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
Full description
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
- 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.