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.

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 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 Do

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 May Be a Fit If

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 (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 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

Bonus Points If You

Have worked with Large Language Models (LLMs), retrieval-augmented generation (RAG), embeddings, or agentic AI systems

Have fine‑tuned foundation models or worked with prompt engineering techniques

Are familiar with ML infrastructure tools such as MLflow, Weights & Biases, Airflow, Kubeflow, or SageMaker

Have experience with vector databases and semantic search technologies

Have healthcare, pharmacy, or health tech experience

Have worked in a startup or other fast‑paced environment

Technologies 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 office

Benefits & 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

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