Senior Machine Learning Engineer, Forecasting

Role overview This senior engineering role focuses on building and operating the production machine learning platform behind a healthcare computer vision and forecasting product. The position sits at the intersection of MLOps, infrastructure, and applied forecasting, shaping how models move from research into reliable, automated systems that clinical teams depend on every day. It is a startup-paced environment with end-to-end ownership of the ML lifecycle.

Responsibilities - Partner with engineering, product, and data science colleagues to identify where ML and AI can address real workflow challenges - Build internal tools and automation that reduce manual effort, accelerate experimentation, and minimize human error - Design, integrate, and monitor large-scale distributed ML systems across the full lifecycle - Investigate model behavior and root-cause data quality and performance regressions - Own and enhance the forecasting ML pipeline, including weekly automated retraining and deployment of multiple production models - Mentor teammates on MLOps practices, automation, and production-grade ML engineering

Requirements - 5+ years building and operating production ML systems, with deep MLOps, deployment automation, and model serving experience - Strong software engineering foundation in Python, containerization (Docker/Kubernetes), CI/CD (GitHub Actions, ArgoCD), and infrastructure-as-code (Terraform, Helm) - Hands-on production deployment skills, including training orchestration (Dagster, Airflow, or similar), automated retraining pipelines, and A/B or variant testing - Systems design experience with scalable microservices, API design, and complex service dependencies - Proven ownership mindset, taking projects from concept through production and continuously improving reliability - Collaborative style with data scientists, backend engineers, and product teams, plus a commitment to tested, well-documented code

Nice to have - Background in healthcare or other regulated industries - Forecasting or time-series modeling experience - Computer vision experience - DAG frameworks and Flink familiarity

Benefits and work setup - Competitive compensation with stock options - Flexible vacation policy with a culture that values rest and recharging - Remote-first setup with virtual and occasional in-person team events - Comprehensive health, dental, and vision insurance - 16 weeks of parental leave for all parents