Staff/Senior Machine Learning Engineer, Clinical AI

About the role

Tempus is seeking a highly skilled and innovative Staff/Senior Machine Learning Engineer to join our Clinical AI Team. The ideal candidate will leverage and deploy cutting-edge natural language processing models and Large Language Models (LLMs) specifically tailored for healthcare applications at scale.

Responsibilities

• Build and operate production AI pipelines: LLM-powered extraction, batch orchestration, and inference, with a focus on reliability, cost, and latency
• Design and maintain Airflow-based orchestration for batch clinical workflows
• Build the observability (metrics, logging, alerting) that catches regressions before they reach downstream consumers
• Build and maintain evaluation infrastructure that measures clinical model output quality continuously: regression detection, drift, gold-set management, dashboards
• Ship platform tooling and SDKs that accelerate Machine Learning Scientists and downstream consumers
• Partner with Machine Learning Scientists to debug bad model outputs to root cause (data, prompt, or pipeline)
• Participate in the pod's on-call rotation
• Collaborate with platform / infrastructure teams to leverage GCP services for performance, security, and cost-efficiency
• Author and review design docs for cross-pod work

Requirements

• Strong command of Python in production environments
• Experience designing, building, and integrating with microservices in production
• Deployed data orchestration workflows in production (Airflow or equivalent)
• Worked on cloud-native services (GCP preferred but not required)
• Built monitoring, observability, and alerting for production systems
• Hands-on experience with at least one major ML framework — we primarily use LangGraph; PyTorch, spaCy, or equivalents are equally welcome
• Strong written and verbal communication, including experience authoring and reviewing design docs (RFCs, PRDs, or equivalent); partners well with research scientists, PMs, and clinicians

Preferred Qualifications

• Operated production systems hands-on — on-call rotations, incident response, postmortems
• Experience building eval / quality measurement systems for ML or LLM outputs
• Built internal platforms or SDKs that other engineers / scientists depended on
• Experience working with clinical or biomedical data (EHR, genomics, pathology, clinical notes)
• Contributions to relevant open-source projects