Machine Learning Engineer

About PlenfulFresh off a $50M Series B backed by Notable Capital, Bessemer Venture Partners, TQ Ventures, and Susa/Kivu Ventures, Plenful is building a category-defining AI workflow automation platform for healthcare operations. Built by healthcare operators for healthcare operators, Plenful empowers care teams across 90+ leading health systems, pharmacies, and payors to eliminate manual administrative work, improve compliance, and unlock critical revenue for patient care.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 full end-to-end lifecycle—from experimentation to production deployment and ongoing model performance monitoring—delivering intelligent services that automate healthcare workflows and directly impact customers.What You'll DoProduction Model Deployment: Design, build, and deploy machine learning models and intelligent services using modern NLP, LLMs, classification, recommendation, and prediction techniques.ML Pipelines & Infrastructure: Develop scalable ML pipelines for training, evaluation, monitoring, inference, and automated retraining while optimizing latency and infrastructure costs.Data & Feature Engineering: Work with structured and unstructured healthcare datasets to build production-ready features and pipeline integrations.Cross-Functional Collaboration: Partner closely with software engineers, product managers, and data teams to translate customer workflow problems into practical ML solutions.Continuous Innovation: Stay current with advancements in machine learning and AI to bring high-impact, practical innovations into the platform architecture.QualificationsRequirementsExperience: 5+ years of professional software engineering or machine learning engineering experience.Education: Bachelor's degree in Computer Science, Machine Learning, Engineering, Mathematics, or equivalent practical experience.Programming & Systems: Strong programming experience in Python, SQL, and containerized deployments (Docker, Kubernetes).Production ML & Data: Proven experience building/deploying ML models to production and writing data pipelines using distributed data tools.Modern MLOps & LLMOps: Familiarity with:*Classical MLOps:* MLflow, Weights & Biases, Airflow*LLMOps:* LangFuse/LangSmith (tracing), Ragas/Braintrust (evals), vLLM/BentoML (serving)*Vector DBs:* Pinecone, Weaviate, Qdrant (for RAG pipelines)Software Fundamentals: Strong grasp of cloud platforms (AWS, GCP, Azure), REST APIs, version control, testing, and CI/CD pipelines.Preferred / Bonus PointsHands-on experience with LLMs, Retrieval-Augmented Generation (RAG), embeddings, fine-tuning, or agentic AI systems.Experience with semantic search technologies and prompt engineering techniques.Domain background in healthcare, pharmacy, or health tech.Experience in fast-paced startup or high-growth environments.Tech StackLanguages & ML Frameworks: Python, PyTorch, TensorFlow, Scikit-learnData & Databases: SQL, PostgreSQL, Vector Databases (Pinecone, Weaviate, Qdrant)DevOps & Cloud: Docker, Kubernetes, AWS, GitHub Actions, REST APIsLLM Ecosystem: OpenAI APIs, Anthropic APIs, LangFuse, LangSmith, vLLM, BentoMLLocation, Culture & BenefitsWorking ModelHybrid Model (San Francisco): Remote-first organization with hub presence in SF and NYC. R&D roles follow a hybrid schedule requiring 2 days per week in the San Francisco office.Featured BenefitsCompensation & Equity: Competitive salary, company equity for all full-time employees, and a 401(k) with a 50% match on the first 3% contributed.Health Coverage: Full medical, dental, and vision insurance coverage with family participation options.Time Off & Leave: Unlimited PTO policy and paid parental leave.Stipends & Perks:Daily Lunch: $100/week lunch stipendWellness: $100/month wellness stipendCommuter: $100/month commuter stipend for SF and NYC employees