{"id":25980,"url":"https://alion.io/job/plenful-machine-learning-engineer","title":"Senior 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":"senior","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":159000,"max_usd":303000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1213},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"BentoML","optional":false},{"name":"Braintrust","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"Langfuse","optional":false},{"name":"LangSmith","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NLP","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"Ragas","optional":false},{"name":"SQL","optional":false},{"name":"vLLM","optional":false},{"name":"Weaviate","optional":false},{"name":"Weights & Biases","optional":false},{"name":"AI Agents","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"Anthropic","optional":true},{"name":"Embeddings","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Kubeflow","optional":true},{"name":"OpenAI","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"PyTorch","optional":true},{"name":"Rest API","optional":true},{"name":"Scikit-learn","optional":true},{"name":"Semantic Search","optional":true},{"name":"Semantic Search","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-07-29T18:46:58Z","employer_posted_date":"2026-07-29","last_verified_at":"2026-10-16T12:45:25Z","board_verified":true,"closed_at":null,"days_open":78,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":78},"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\nWe're looking for a Senior Machine Learning Engineer to design, build, and deploy production-grade ML systems that power the next generation of Plenful's AI platform. You'll own the end-to-end lifecycle - from experimentation to production deployment to ongoing model performance.\nYou'll partner closely with software engineers, product managers, and data teams to build models and intelligent services that automate healthcare workflows, improve operational efficiency, and create great user experiences. This is an engineering-focused role, and your work will directly impact customers.\nYou'll thrive here if you enjoy solving hard problems with practical engineering solutions, take ownership from idea through production, and balance experimentation with delivering reliable software. We're a fast-moving startup where priorities evolve quickly - you should be energized by that, not worn down by it.\nWhat You'll Do\nDesign, build, and deploy machine learning models into production\n\nDevelop scalable ML pipelines for training, evaluation, monitoring, and inference\n\nBuild intelligent services using modern NLP, LLM, classification, recommendation, and prediction techniques where appropriate\n\nCollaborate with Product and Engineering to translate customer problems into ML solutions\n\nImprove model performance through experimentation, feature engineering, and evaluation\n\nWork with structured and unstructured datasets to develop production-ready features\n\nImplement monitoring, observability, and retraining strategies to maintain model quality\n\nOptimize model latency, scalability, and infrastructure costs\n\nContribute to architecture discussions and engineering best practices\n\nStay current with advancements in machine learning and AI, and bring practical innovations into our platform\n\nYou May Be a Fit If\nYou have 5+ years of professional software engineering or machine learning engineering experience\n\nYou have a Bachelor's degree in Computer Science, Machine Learning, Engineering, Mathematics, or a related technical field (or equivalent practical experience)\n\nYou have strong programming experience in Python\n\nYou've built and deployed machine learning models into production environments\n\nYou have a solid understanding of supervised and unsupervised learning techniques\n\nYou're familiar with modern ML infrastructure - classical MLOps (MLflow, Weights & Biases, Airflow) and LLMOps (LangFuse/LangSmith for tracing, Ragas/Braintrust for evaluation, vLLM/BentoML for serving, and a vector database such as Pinecone, Weaviate, or Qdrant for RAG pipelines)\n\nYou've built data pipelines using SQL and distributed data processing tools\n\nYou're familiar with cloud platforms such as AWS, GCP, or Azure\n\nYou've deployed containerized applications using Docker and Kubernetes\n\nYou have a strong grasp of software engineering fundamentals - testing, version control, and CI/CD\n\nYou communicate well and collaborate easily across technical and non-technical teams\n\nBonus points if you:\nHave worked with Large Language Models (LLMs), retrieval-augmented generation (RAG), embeddings, or agentic AI systems\n\nHave fine-tuned foundation models or worked with prompt engineering techniques\n\nAre familiar with ML infrastructure tools such as MLflow, Weights & Biases, Airflow, Kubeflow, or SageMaker\n\nHave experience with vector databases and semantic search technologies\n\nHave healthcare, pharmacy, or health tech experience\n\nHave worked in a startup or other fast-paced environment\n\nTechnologies you'll likely work with: Python, PyTorch, TensorFlow, Scikit-learn, SQL, PostgreSQL, Docker, Kubernetes, AWS, GitHub Actions, REST APIs, vector databases, and LLM APIs (OpenAI, Anthropic, etc.)\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":5941,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"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":"2026-07-29T18:46:58Z"}],"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":24,"band":"cold","label":"Long shot","p_open":1,"p_active":0.66,"p_room":0.36,"age_days":78,"expected_fill_days":42,"reasons":["conf:2","win:tail","crowd:"],"computed_at":"2026-10-16T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/plenful-machine-learning-engineer","json_url":"https://alion.io/job/plenful-machine-learning-engineer.json","meta":{"generated_at":"2026-10-16T14:51:11Z","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":14539,"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-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-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-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-machine-learning-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fplenful-machine-learning-engineer"}]}