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# Staff/Senior Machine Learning Engineer, Clinical AI

- **Company:** Tempus Inc
- **Location:** Boston, MA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Code Review, 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-boston-ma-usa-58888639

## About the Role

Experteer Overview As a Staff/Senior Machine Learning Engineer, you will design and operate production AI pipelines and NLP/LLM solutions for healthcare at scale. You'll enhance clinical workflows, trial matching, and medical research by delivering reliable, observability-driven systems. You'll collaborate with ML scientists, clinicians, and platform teams to ship tools and documentation that accelerate research and clinical impact. This role offers the chance to shape healthcare delivery through cutting-edge AI in a cross-functional environment. Compensation / Benefits * Build and operate production AI pipelines: LLM-powered extraction, batch orchestration, and inference * Design and maintain Airflow-based orchestration for batch clinical workflows * Build observability (metrics, logging, alerting) to catch regressions * Build and maintain eval infrastructure to measure clinical model output quality * Ship platform tooling and SDKs for ML scientists and downstream users * Partner a
and ML Scientists to debug model outputs to root causes (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 reviews 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) * 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 (RFCs, PRDs, or equivalent) Key requirements *

## Description

Experteer Overview As a Staff/Senior Machine Learning Engineer, you will design and operate production AI pipelines and NLP/LLM solutions for healthcare at scale. You'll enhance clinical workflows, trial matching, and medical research by delivering reliable, observability-driven systems. You'll collaborate with ML scientists, clinicians, and platform teams to ship tools and documentation that accelerate research and clinical impact. This role offers the chance to shape healthcare delivery through cutting-edge AI in a cross-functional environment. Compensation / Benefits * Build and operate production AI pipelines: LLM-powered extraction, batch orchestration, and inference * Design and maintain Airflow-based orchestration for batch clinical workflows * Build observability (metrics, logging, alerting) to catch regressions * Build and maintain eval infrastructure to measure clinical model output quality * Ship platform tooling and SDKs for ML scientists and downstream users * Partner with ML Scientists to debug model outputs to root causes (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 reviews 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) * 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 (RFCs, PRDs, or equivalent) Key requirements *


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