Staff/Senior Machine Learning Engineer, Clinical AI

Tempus AI is a company focused on precision medicine and advancing the healthcare industry. They are seeking a highly skilled Staff/Senior Machine Learning Engineer to join their Clinical AI Team, where the role involves leveraging and deploying natural language processing models and LLMs to optimize clinical workflows and improve medical research.

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 eval 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
• Raise the engineering bar through code review and design review

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
• Operated production systems hands-on — on-call rotations, incident response, postmortems
• Experience building eval / quality measurement systems for ML or LLM outputs
• Hands-on production LLM application experience (prompts, agents, RAG, LLM evals, extraction pipelines)
• 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