Machine Learning Engineer
About Plenful
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. Apply now to join our growing team.
We're looking for a Machine Learning Engineer to help design, build, and deploy production-grade machine learning systems that power the next generation of Plenful's AI platform.
You'll partner closely with software engineers, product managers, and data teams to develop models and intelligent services that automate healthcare workflows, improve operational efficiency, and create exceptional user experiences. This is an engineering-focused role where you'll own the end-to-end lifecycle—from experimentation to production deployment and ongoing model performance.
If you're excited about applying modern machine learning techniques in a fast-moving startup environment where your work directly impacts customers, we'd love to meet you.
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, bringing practical innovations into our platform.
What We're Looking For
Required Qualifications
5+ years of professional software engineering or machine learning engineering experience.
Bachelor's degree in Computer Science, Machine Learning, Engineering, Mathematics, or a related technical field (or equivalent practical experience).
Strong programming experience in Python.
Experience building and deploying machine learning models into production environments.
Solid understanding of supervised and unsupervised learning techniques.
Familiarity with modern ML infrastructure spanning 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/Qdrant for RAG pipelines).
Experience building data pipelines using SQL and distributed data processing tools.
Familiarity with cloud platforms such as AWS, GCP, or Azure.
Experience deploying containerized applications using Docker and Kubernetes.
Strong understanding of software engineering fundamentals, testing, version control, and CI/CD.
Excellent communication skills with the ability to collaborate across technical and non-technical teams.
Preferred Qualifications
Experience working with Large Language Models (LLMs), retrieval-augmented generation (RAG), embeddings, or agentic AI systems.
Experience fine-tuning foundation models or working with prompt engineering techniques.
Familiarity with ML infrastructure tools such as MLflow, Weights & Biases, Airflow, Kubeflow, or SageMaker.
Experience with vector databases and semantic search technologies.
Healthcare, pharmacy, or health tech experience.
Experience working 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
LLM APIs (OpenAI, Anthropic, etc.)
What Makes You Successful
You enjoy solving difficult problems with practical engineering solutions.
You're comfortable taking ownership from idea through production.
You balance experimentation with delivering reliable software.
You communicate well and enjoy collaborating across functions.
You thrive in fast-moving startup environments where priorities evolve quickly.
You care deeply about product quality, customer outcomes, and continuous learning.
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