{"id":2632617,"url":"https://alion.io/job/plenful-staff-machine-learning-engineer","title":"Staff Machine Learning Engineer","company":{"id":6924,"name":"Plenful","domain":"plenful.com","url":"https://alion.io/company/plenful","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":194000,"max_usd":396000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":823},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"DeepSpeed","optional":false},{"name":"Docker","optional":false},{"name":"FSDP","optional":false},{"name":"Function Calling","optional":false},{"name":"HIPAA","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Distillation","optional":false},{"name":"Pinecone","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SFT","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Tool Use","optional":false},{"name":"vLLM","optional":false},{"name":"LangChain","optional":true}],"status":"live","first_seen_at":"2026-10-15T15:24:03Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-16T12:45:25Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About Plenful\nPlenful 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.\nBuilt 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.\nAbout the role\nHealthcare'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.\nAt 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.\nWhat you'll do\nYou set the direction for how our agents are built\nDefine the architecture for production agents: tool use, retrieval, state and context management, routing, orchestration, tracing and failure recovery.\n\nTurn what works into patterns other engineers reuse.\n\nMake the calls on build versus buy for models and platform pieces.\n\nYou own our post-training strategy\nDecide where supervised fine-tuning, preference optimization, distillation and reinforcement learning pay off, and where they do not.\n\nTrain and ship LLMs and vision-language models for healthcare tasks, and build the in-house capability to keep doing it.\n\nProve every gain with evaluation that holds up in production.\n\nYou define what good looks like\nSet the AI quality strategy: automated graders, calibrated LLM judges, regression suites, human review, failure taxonomies and offline-to-online validation.\n\nMake eval results the basis for release decisions.\n\nYou build loops that improve themselves\nDesign the systems that turn production failures and user feedback into better datasets, evals and models.\n\nSet the standard for dataset versioning, observability and MLOps so every team's work is reproducible and measurable.\n\nYou raise the bar for everyone\nCo-own the 12-month ML roadmap with engineering and product leaders, balancing near-term wins against longer bets.\n\nLead technical reviews, mentor Senior engineers, and help us hire.\n\nBuild privacy in by design: minimum necessary data, de-identified or synthetic data where it works, and HIPAA-aware practices throughout.\n\nWhat we're looking for\n8+ years in ML or software engineering, including 3+ years leading LLM or agentic systems in production.\n\nDeep 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.\n\nYou have designed an evaluation strategy for LLM or agent systems and seen it drive real release decisions.\n\nYou 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.\n\nStrong Python and PyTorch, and comfort with distributed training and serving (for example vLLM, FSDP or DeepSpeed).\n\nReal MLOps depth: Docker, Kubernetes, MLflow or equivalent, CI/CD, and experiment and dataset tracking.\n\nYou influence without authority. Your design docs, reviews and mentoring make other engineers better.\n\nYou stay current, and others rely on it\nYou use new models and coding agents the week they ship, and colleagues ask you whether a new thing is real.\n\nYou turn papers and releases into experiments within days and report back with numbers.\n\nYou have proof: open source, write-ups, talks, or evals other people use.\n\nNice to have\nHealthcare data experience: EHR, FHIR, claims, prior authorization.\n\nPublications or open-source work in LLM evaluation, post-training or agents.\n\nExperience interviewing and hiring ML engineers.\n\nWork in another regulated, high-stakes domain.\n\nWhat your first 90 days look like\nBy 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.\nWhy You'll Love Working Here\nMission-Driven, World-Class Team - Join an exceptional group of professionals aligned around a meaningful mission and committed to making an impact\n\nOpportunities for Growth - Strengthen your expertise through collaboration with experienced, high-performing leaders across the organization\n\nFlexible 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\n\nBenefits & Perks\nHealthcare Coverage - Full medical, dental, and vision insurance for you and participation for your family\n\n401(k) with Company Match - Plenful matches 50% of your first 3% contributed\n\nEquity - Every full-time employee shares in our success\n\nUnlimited PTO - Take the time you need, when you need it\n\nDaily Lunch Stipend - $100/week to cover your midday meals\n\nWellness Stipend - $100/month to support your health and well-being\n\nCommuter Benefits - $100/month for SF and NYC-based employees\n\nParental Leave - Paid leave to support growing families","description_format":"text","description_chars":6195,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity","Hybrid work","Parental leave","Unlimited PTO","Vision insurance"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Medical AI","Hospital Operations Software"],"lifecycle":[{"event":"open","at":"2026-10-15T16:35:13Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 2","filings_12m":2,"filings_prev_12m":3,"green_card_filings_12m":0,"median_offered_wage_usd":162500,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":2}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":42,"reasons":["conf:2","win:early"],"computed_at":"2026-10-16T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/plenful-staff-machine-learning-engineer","json_url":"https://alion.io/job/plenful-staff-machine-learning-engineer.json","meta":{"generated_at":"2026-10-16T13:35:59Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":13093,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-17T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":6924},"rest":"https://alion.io/mcp/rest/get_company?id=6924"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fplenful-staff-machine-learning-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fplenful-staff-machine-learning-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fplenful-staff-machine-learning-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/plenful-staff-machine-learning-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fplenful-staff-machine-learning-engineer"}]}