Machine Learning Engineer

About Plenful

Fresh 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.

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About The Role

We'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.

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What You'll Do

• Production 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.

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Qualifications

Requirements

• Experience: 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 Points

• Hands-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.

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Tech Stack

• Languages & ML Frameworks: Python, PyTorch, TensorFlow, Scikit-learn
• Data & Databases: SQL, PostgreSQL, Vector Databases (Pinecone, Weaviate, Qdrant)
• DevOps & Cloud: Docker, Kubernetes, AWS, GitHub Actions, REST APIs
• LLM Ecosystem: OpenAI APIs, Anthropic APIs, LangFuse, LangSmith, vLLM, BentoML

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Location, Culture & Benefits

Working Model

• Hybrid 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 Benefits

• Compensation & 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 stipend
• Wellness: $100/month wellness stipend
• Commuter: $100/month commuter stipend for SF and NYC employees