Director, ML Engineering

Director, ML Engineering Company & Culture Welldoc is at the forefront of digital health, driven by a powerful mission: empowering better cardiometabolic health through AI-powered, personalized digital tech, with a vision to be the leading advanced AI digital technology partner across the healthcare industry. We're a team passionate about leveraging cutting-edge science to improve lives, united by core values of collaborative innovation, accountability to excellence, customer focus, efficiency, and unwavering integrity, quality, and safety. At Welldoc, you'll thrive in a collaborative and innovative environment where your contributions directly impact our mission. Recognized as a Great Place to Work for the past four years and named to Modern Healthcare’s Best Places to Work 2025, as well as being an industry thought leader featured at SXSW and in the Wall Street Journal and Economist, we invite you to make a real difference in healthcare with us. Job Purpose Lead a team of ML Engineers to design, build, deploy, and scale robust, end-to-end machine learning pipelines and systems— spanning classical ML to Generative AI —into production. Be a hands-on technical leader for the ML Engineering team, responsible for the architecture, implementation, and operational management (MLOps) of all AI/ML models. This role will also act as the primary technical bridge to the Bangalore ML team, fostering collaboration and alignment. ** This role is based in Columbia, MD, and we are open to discussing relocation assistance for the right candidate. Responsibilities Team Leadership: Lead, mentor, and manage a team of ML Engineers, including the distributed team in Bangalore, India, to deliver high-impact ML projects from engineering design to production. Technical Execution Leadership: Translate AI and product priorities into robust ML engineering solutions. Architect, design, and implement scalable ML and MLOps systems. Ensure production reliability, performance, and maintainability of all ML systems. Global Team-Building: Act as the primary technical bridge to the Bangalore ML team, fostering a unified engineering culture and ensuring process alignment through regular communication and international travel (approx. 4-5 times per year). MLOps & CI/CD: Define and implement the complete MLOps lifecycle and data value chain using industry best-in-class solutions. Architect and oversee the development of CI/CD pipelines specifically for AI/ML projects, ensuring automated, reliable, and efficient model training and deployment. Architecture: Collaborate with Software Engineering to design and build scalable ML systems, leveraging our data lake architecture (Spark and Databricks) to capture and automate data flows. Production Deployment: Drive the production deployment, monitoring, scaling, and maintenance of statistical, classical ML, deep learning, and generative AI (including LLM/RAG) models. Technical Standards: Provide technical leadership through code reviews and management of best coding practices. Oversee the technical quality and operational excellence of the ML Engineering team's work. Governance & Compliance: Create and maintain data governance SOPs and work instructions such that all data models, processes, and analytic tools conform with required quality, data privacy, and regulatory standards (e.g., ISO13485/MDSAP, HIPAA, GDPR, HiTrust, SOC2). Technology Stack: Leverage a broad stack of technologies—like SQL, Spark, Python, Databricks, PyTorch, and cloud platforms—to build and manage production ML systems. Required Skills & Experience 5-10 years of experience with relational databases and extracting and transforming data using SQL or Spark. 5-10 years of hands-on experience building, deploying, and maintaining production-grade classical and deep learning ML models using Python. Proven experience with deploying Generative AI models , including LLM and RAG applications , in a production environment. 5-10 years of expe ... (truncated, view full listing at source)