Machine Learning Engineer

About PlenfulPlenful 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 workflow 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 90+ leading health systems 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 RoleWe're looking for a 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.You'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.You'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.What You'll DoDesign, build, and deploy machine learning models into productionDevelop scalable ML pipelines for training, evaluation, monitoring, and inferenceBuild intelligent services using modern NLP, LLM, classification, recommendation, and prediction techniques where appropriateCollaborate with Product and Engineering to translate customer problems into ML solutionsImprove model performance through experimentation, feature engineering, and evaluationWork with structured and unstructured datasets to develop production-ready featuresImplement monitoring, observability, and retraining strategies to maintain model qualityOptimize model latency, scalability, and infrastructure costsContribute to architecture discussions and engineering best practicesStay current with advancements in machine learning and AI, and bring practical innovations into our platformYou May Be a Fit IfYou have 5+ years of professional software engineering or machine learning engineering experienceYou have a Bachelor's degree in Computer Science, Machine Learning, Engineering, Mathematics, or a related technical field (or equivalent practical experience)You have strong programming experience in PythonYou've built and deployed machine learning models into production environmentsYou have a solid understanding of supervised and unsupervised learning techniquesYou'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)You've built data pipelines using SQL and distributed data processing toolsYou're familiar with cloud platforms such as AWS, GCP, or AzureYou've deployed containerized applications using Docker and KubernetesYou have a strong grasp of software engineering fundamentals — testing, version control, and CI/CDYou communicate well and collaborate easily across technical and non-technical teamsBonus Points If YouHave worked with Large Language Models (LLMs), retrieval-augmented generation (RAG), embeddings, or agentic AI systemsHave fine-tuned foundation models or worked with prompt engineering techniquesAre familiar with ML infrastructure tools such as MLflow, Weights & Biases, Airflow, Kubeflow, or SageMakerHave experience with vector databases and semantic search technologiesHave healthcare, pharmacy, or health tech experienceHave worked in a startup or other fast-paced environmentTechnologies 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.)Why You'll Love Working Here🚀 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 officeBenefits & Perks🏥 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