ML Engineer

Who is DexCare? DexCare optimizes time in healthcare, streamlining patient access, reducing waits, and enhancing overall experiences. Guided by our mission – Nobody waits for care – DexCare addresses tech gaps by aligning supply and demand to modernize healthcare infrastructures for an inclusive ecosystem. We are equally committed to our DEIB mission: Creating an inclusive workplace where diversity drives innovation, equity ensures fairness, and belonging strengthens collaboration, enabling everyone to thrive.    What is DexCare? DexCare, a digital care orchestration platform, streamlines care delivery logistics. It empowers healthcare systems to predict constraints and precisely schedule services, optimizecapacity, and cuts operational costs. Currently serving 57 million patients, including Kaiser Permanente and Providence, DexCare ushers in a new era of digital-care access, ensuring health systems can efficiently track and deliver every hour of capacity for consumer ease.    For more information, visit www.dexcare.com or follow us on LinkedIn. 

About the Role

DexCare is looking for a Machine Learning Engineer II to build the AI systems behind our products. Your primary focus will be Optimize AI, which ingests, structures, and governs complex healthcare scheduling rules and institutional knowledge into a single, verified source of truth. The core problem is turning large volumes of messy, inconsistent, and constantly changing operational knowledge into structured knowledge that people and AI agents can rely on to make decisions.

This is a hands-on, mid-level role. You will build, ship, and own production ML and LLM systems, working closely with product, engineering, and data teams. Our AI products share a common foundation, so you will also work across them, including Mila, our patient-facing agents, as priorities shift. You will join a well-established team early in the build-out of DexCare’s AI capabilities, with large health systems already using our products. If you enjoy taking ML systems from prototype to production and making them accurate, reliable, and fast, we’d love to hear from you.

What You’ll Do

• Build systems that ingest unstructured and inconsistent source material, such as policies, protocols, rules, and spreadsheets, and turn it into structured, versioned, and verified knowledge.
• Build extraction, normalization, and validation pipelines using LLMs and classical techniques; detect conflicts, gaps, and stale information, and route uncertain cases to human review.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines that give downstream systems and agents accurate, grounded, and traceable answers with low latency.