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# Staff/Senior Machine Learning Engineer, Clinical AI

- **Company:** Tempus Inc
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Software Debugging, Software Design Documents, Python (Programming Language), Machine Learning, Tensorflow, 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-seattle-wa-usa-58888641

## About the Role

Experteer Overview In this role you will develop and deploy NLP and LLM-powered healthcare solutions at scale to support clinical workflows and research. You will collaborate with ML scientists and platform teams to build reliable production pipelines, observability, and evaluation tooling that ensure high-quality model outputs. You will help optimize trial matching, clinical decision support, and medical research, contributing to Tempus's mission to deliver real-time, actionable insights to physicians. This position offers the chance to shape AI-driven care through scalable, secure cloud-native systems. Compensation / Benefits * Build and operate production AI pipelines for LLM-powered extraction, orchestration, and inference * Design and maintain Airflow orchestration for batch clinical workflows * Develop observability with metrics, logging, and alerts to catch regressions * Create evaluation infrastructure to monitor clinical model output quality (drift, regressions, dashboards) *
Ship platform tooling and SDKs to accelerate ML Scientists and downstream users * Collaborate with ML Scientists to debug model outputs to root causes (data, prompts, or pipelines) * Participate in pod 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 * Elevate engineering standards via code/design reviews Tasks * Strong Python production experience * Experience designing, building, and integrating with microservices * Deployed data orchestration workflows in production (Airflow or equivalents) * Experience with cloud-native services (GCP preferred) * Built monitoring/observability/alerting for production systems * Hands-on experience with major ML frameworks (LangGraph preferred; PyTorch or spaCy acceptable) * Strong written and verbal communication; experience writing design docs (RFCs/PRDs) Key requirements *

## Description

Experteer Overview In this role you will develop and deploy NLP and LLM-powered healthcare solutions at scale to support clinical workflows and research. You will collaborate with ML scientists and platform teams to build reliable production pipelines, observability, and evaluation tooling that ensure high-quality model outputs. You will help optimize trial matching, clinical decision support, and medical research, contributing to Tempus's mission to deliver real-time, actionable insights to physicians. This position offers the chance to shape AI-driven care through scalable, secure cloud-native systems. Compensation / Benefits * Build and operate production AI pipelines for LLM-powered extraction, orchestration, and inference * Design and maintain Airflow orchestration for batch clinical workflows * Develop observability with metrics, logging, and alerts to catch regressions * Create evaluation infrastructure to monitor clinical model output quality (drift, regressions, dashboards) * Ship platform tooling and SDKs to accelerate ML Scientists and downstream users * Collaborate with ML Scientists to debug model outputs to root causes (data, prompts, or pipelines) * Participate in pod 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 * Elevate engineering standards via code/design reviews Tasks * Strong Python production experience * Experience designing, building, and integrating with microservices * Deployed data orchestration workflows in production (Airflow or equivalents) * Experience with cloud-native services (GCP preferred) * Built monitoring/observability/alerting for production systems * Hands-on experience with major ML frameworks (LangGraph preferred; PyTorch or spaCy acceptable) * Strong written and verbal communication; experience writing design docs (RFCs/PRDs) Key requirements *


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