> Markdown version of [/jobs/ext/2010951-staff-senior-machine-learning-engineer-clinical-ai](https://www.wearedevelopers.com/jobs/ext/2010951-staff-senior-machine-learning-engineer-clinical-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff/Senior Machine Learning Engineer, Clinical AI - **Company:** Tempus Inc - **Location:** Chicago, IL, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, 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-chicago-il-usa-58888638 ## About the Role _ SDKs to accelerate ML Scientists and downstream users * Collaborate with ML Scientists to diagnose and root-cause bad model outputs * Participate in on-call rotations * Collaborate with platform/infrastructure teams to leverage GCP services for performance, security, and cost efficiency * Author and review design docs for cross-pod work * Contribute to code and design reviews to raise engineering standards Tasks * Strong command of Python in production * Experience designing, building, and integrating with microservices in production * Deployed data orchestration workflows (Airflow or equivalent) * Cloud-native services experience (GCP preferred) * Monitoring, observability, and alerting for production systems * Hands-on experience with major ML frameworks (LangGraph; PyTorch, spaCy, or equivalents) Key requirements * incentive compensation * restricted stock units * medical benefits * remote-friendly options * position-dependent benefits ## Description Experteer Overview In this role you will design, deploy, and operate production AI pipelines that power healthcare-focused NLP and LLM applications. You'll contribute to clinical workflows, trial matching, and biomedical research by delivering reliable, scalable AI capabilities. You will work with cross-functional teams to improve real-time insights for physicians and patients, shaping how AI supports clinical decision making. This position offers a chance to innovate at scale in a mission-driven clinical AI environment, with a strong emphasis on observability, reliability, and performance. Compensation / Benefits * Build and operate production AI pipelines for LLM-powered extraction, orchestration, and inference * Design and maintain Airflow-based orchestration for batch clinical workflows * Develop observability (metrics, logging, alerting) to detect regressions * Build and maintain evaluation infrastructure for continuous model output quality assessment * Ship platform tooling and SDKs to accelerate ML Scientists and downstream users * Collaborate with ML Scientists to diagnose and root-cause bad model outputs * Participate in on-call rotations * Collaborate with platform/infrastructure teams to leverage GCP services for performance, security, and cost efficiency * Author and review design docs for cross-pod work * Contribute to code and design reviews to raise engineering standards Tasks * Strong command of Python in production * Experience designing, building, and integrating with microservices in production * Deployed data orchestration workflows (Airflow or equivalent) * Cloud-native services experience (GCP preferred) * Monitoring, observability, and alerting for production systems * Hands-on experience with major ML frameworks (LangGraph; PyTorch, spaCy, or equivalents) Key requirements * incentive compensation * restricted stock units * medical benefits * remote-friendly options * position-dependent 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. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)