Machine Learning Engineer

Plenful 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.

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 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 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.

• Design, build, and deploy machine learning models into production
• Develop scalable ML pipelines for training, evaluation, monitoring, and inference
• Build intelligent services using modern NLP, LLM, classification, recommendation, and prediction techniques where appropriate
• Collaborate with Product and Engineering to translate customer problems into ML solutions
• Improve model performance through experimentation, feature engineering, and evaluation
• Work with structured and unstructured datasets to develop production‑ready features
• Implement monitoring, observability, and retraining strategies to maintain model quality
• Optimize model latency, scalability, and infrastructure costs
• Contribute to architecture discussions and engineering best practices
• Stay current with advancements in machine learning and AI, and bring practical innovations into our platform

• You have 5+ years of professional software engineering or machine learning engineering experience
• You 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 Python
• You've built and deployed machine learning models into production environments
• You have a solid understanding of supervised and unsupervised learning techniques
• You're familiar with modern ML infrastructure — classical MLOps (MLflow, Weights & Biases, Airflow) and LLMOps (Lang Fuse/Lang Smith 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 tools
• You're familiar with cloud platforms such as AWS, GCP, or Azure
• You've deployed containerized applications using Docker and Kubernetes
• You have a strong grasp of software engineering fundamentals — testing, version control, and CI/CD
• You communicate well and collaborate easily across technical and non-technical teams

• Have worked with Large Language Models (LLMs), retrieval-augmented generation (RAG), embeddings, or agentic AI systems
• Have fine‑tuned foundation models…