Machine Learning Engineer

About Plenful

Plenful is on a mission to transform healthcare operations from the inside out. Founded with a $50M Series B and backed by leading investors including Notable Capital, Bessemer Venture Partners, TQ Ventures, Susa/Kivu Ventures, and others, Plenful builds the AI workflow automation platform that healthcare teams rely on to operate smarter, faster, and more efficiently. Our technology eliminates manual work, reduces administrative burden, and improves compliance, all while unlocking critical revenue for patient programs. Driven by a deep understanding of healthcare challenges, Plenful serves 90+ leading health systems across the country.

About The Role

We are seeking a Machine Learning Engineer to design, build, and deploy production-grade ML systems that power the next generation of Plenful's AI platform. You will own the end-to-end lifecycle from experimentation to production deployment to ongoing model performance. You will collaborate 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 where your work directly impacts customers. You will 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.

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
• Collaborate with Product and Engineering to translate customer problems into ML solutions
• Improve model performance through experimentation, feature engineering, and evaluation
• Develop production-ready features from structured and unstructured datasets
• 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

• 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
• Built and deployed machine learning models into production environments
• Solid understanding of supervised and unsupervised learning techniques
• Familiarity with modern ML infrastructure (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)
• Data pipelines using SQL and distributed data processing tools
• Familiarity with cloud platforms such as AWS, GCP, or Azure
• Deployed containerized applications using Docker and Kubernetes
• Strong grasp of software engineering fundamentals (testing, version control, CI/CD)
• Communicates well and collaborates easily across technical and non-technical teams

Bonus Points

• Experience with Large Language Models (LLMs), retrieval-augmented generation (RAG), embeddings, or agentic AI systems
• Fine-tuned foundation models or worked 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
• 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
• GCP
• Azure
• GitHub Actions
• REST APIs
• vector databases
• LLM APIs (OpenAI, Anthropic, etc.)