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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff/Senior Machine Learning Engineer, Clinical AI - **Company:** Tempus Inc - **Location:** New York, NY, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Software Debugging, Python (Programming Language), Machine Learning, Systems Integration, Data Logging, Pytorch, Large Language Models, Machine Learning Operations, Spacy, Serverless Computing, Microservices - **Published:** August 10, 2026 - **Apply:** https://us.experteer.com/career/view-jobs/staff-senior-machine-learning-engineer-clinical-ai-new-york-city-ny-usa-58888637 ## About the Role AI regressions * Create and maintain eval infrastructure to measure clinical model output quality, including drift and regression detection * Ship platform tooling and SDKs to accelerate ML Scientists and downstream consumers * Collaborate with ML Scientists to root-cause bad model outputs (data, prompts, or pipelines) * 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 and design reviews Tasks * 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 (LangGraph; PyTorch, spaCy, or equivalents) * Strong written and verbal communication, including authoring and reviewing design docs Key requirements * incentive compensation * restricted stock units * medical benefits ## Description Experteer Overview As a Staff/Senior Machine Learning Engineer on Tempus's Clinical AI Team, you will build and operate production AI pipelines centered on LLMs and healthcare-specific NLP. You will design scalable systems that improve clinical workflows, trial matching, and medical research, delivering real-time, actionable insights to clinicians. You'll work closely with researchers and engineers to debug model outputs, monitor quality, and ensure secure, cost-efficient cloud deployments. This role offers the chance to shape AI tooling that accelerates clinical decision-making at scale, impacting patient care. You will join a mission-driven team developing real-world evidence platforms that connect data to 0 Compensation / Benefits * Build and operate production AI pipelines for LLM-powered extraction, batch orchestration, and inference * Design and maintain Airflow-based orchestration for batch clinical workflows * Develop observability through metrics, logging, and alerting to catch regressions * Create and maintain eval infrastructure to measure clinical model output quality, including drift and regression detection * Ship platform tooling and SDKs to accelerate ML Scientists and downstream consumers * Collaborate with ML Scientists to root-cause bad model outputs (data, prompts, or pipelines) * 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 and design reviews Tasks * 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 (LangGraph; PyTorch, spaCy, or equivalents) * Strong written and verbal communication, including authoring and reviewing design docs Key requirements * incentive compensation * restricted stock units * medical benefits ## Related Videos - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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