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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff/Senior Machine Learning Engineer - **Company:** Tempus Inc - **Location:** New York, NY, United States (Remote available) - **Experience:** Expert - **Salary:** $170,000.0 - $230,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Code Review, Software Debugging, Python (Programming Language), Machine Learning, Natural Language Processing, Systems Integration, Data Logging, Google Cloud, Pytorch, Large Language Models, Electronic Medical Records, Machine Learning Operations, Spacy, Serverless Computing, Microservices - **Published:** June 9, 2026 - **Apply:** https://www.dice.com/job-detail/91efd158-1b68-46b9-85ef-4ce19e657e33 ## About the Role * 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 (Google Cloud Platform 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 Preferred Qualifications: * 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 ## Description We're seeking a highly skilled and innovative Staff/Senior Machine Learning Engineer to join our Clinical AI Team. As a Staff/Senior Machine Learning Engineer, you'll play a crucial role in leveraging and deploying cutting-edge natural language processing models and LLMs specifically tailored for healthcare applications at scale. Your work will contribute to optimizing clinical workflows, improving clinical trial matching, and advancing medical research. This position offers an exciting opportunity to leverage the power of natural language processing and LLMs to revolutionize healthcare and make a significant impact on people's lives. What You Will Do: * 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 Google Cloud Platform 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 ## Related Videos - [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) - [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) - [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 – 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) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [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) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)