Machine Learning Engineer

Role Overview

Local Infusion is building a next-generation operating system for infusion care powered by automation, AI, and real-time insights. As a Machine Learning Engineer, you will own the intelligence layer of the platform—using structured and unstructured data to drive automation and predictions that help patients start treatment faster.

Responsibilities

• Model Development: Build and deploy ML models to:
  • identify missing data
  • predict treatment delays
  • triage high-risk referrals
  • score payer/patient friction
• AI Integration: Architect and maintain LLM-powered workflows
• Data Infrastructure: Build and maintain pipelines to collect, clean, label, and store data across systems
• Model Monitoring & Improvement:
  • define success metrics
  • monitor model performance over time
  • iterate to improve accuracy and reliability
  • set up training data and data labeling/feedback loops
• Cross-Functional Partnership: Collaborate with product, engineering, and operations to identify high-leverage automation opportunities and embed models into workflows

Requirements

• 6+ years in ML engineering, data science, or applied AI (healthcare experience is a bonus)
• Proficient in Python (Pandas, scikit-learn, LangChain, PyTorch or TensorFlow) and SQL
• Strong experience building and deploying production ML models
• Experience with LLM integration, RAG, and vector search (e.g., Pinecone, FAISS)
• Experience with unstructured data (OCR, NLP, document/form understanding)
• Cloud infrastructure experience (AWS preferred), plus Git and basic DevOps practices
• Comfortable partnering with product and operations to solve real-world business problems
• Healthcare data experience: EMR/EHR, insurance claims, HL7/FHIR
• Experience building patient/payer-facing AI in regulated environments (e.g., HIPAA)

Benefits (Summary)

• Employer-sponsored medical, dental, and vision
• Short/long-term disability coverage
• 401(k) with match, 15 days PTO
• Competitive paid parental leave and flexible return-to-work policy
• Professional development opportunities
• Remote-first culture