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# Senior Software Engineer, Data Products

- **Company:** Lynx MD, Ltd.
- **Location:** Palo Alto, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Clinical Data Repository, Cloud Computing, Data Governance, Distributed Systems, Fault Tolerance, Python (Programming Language), Meta-Data Management, Software Architecture, Strategies of Testing, EHR Systems, Fast Healthcare Interoperability Resources, Large Language Models, Electronic Medical Records, Information Technology, Low Latency, Health Level Seven International, Build Tools, Machine Learning Operations, Data Pipelines
- **Published:** July 21, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0829936462b61939

## About the Role

* 5+ years building production software systems; care deeply about coding craft

 * Fluent in Python, MCP and high scale / high throughout data pipelines

 * Strong grasp of distributed systems, software architecture, and engineering fundamentals

 * Experienced in building complex data pipelines and cloud-native systems on AWS

 * Think holistically about correctness, performance, security, and cost

 * Self-directed, clear communicator, thrive in a fast-moving startup with high ownership

 * Genuinely excited by the mission: applying great engineering to improve patient outcomes

EXTRA CREDIT

 * Experience in building a Large Language Model (LLM) pipeline (e.g., Qwen, Llama)

 * Experience working with medical or clinical data (EHR, FHIR, HL7, claims, or de-identified patient datasets)

 * Familiarity with data standardization frameworks, data cataloguing, or metadata management

 * Experience with HIPAA compliance, data privacy, or healthcare security requirements

 * Exposure to ML pipelines, feature stores, or working with data science teams

 * A bachelor's, master's, or PhD in Computer Science or a related quantitative field

## Description

* Design and architect complex, scalable systems end-to-end. Making principled tradeoffs between performance, reliability, maintainability, and cost, and owning those decisions from whiteboard to production

 * Design, build, and own scalable data pipelines for real-world health data from diverse sources

 * Architect and implement production-grade AI-driven services for high-scale analysis of medical data.

 * Build and operate robust backend services, APIs, and data products end-to-end. You design it, you build it, you run it

 * Tackle genuinely hard distributed systems problems: low-latency access patterns, fault tolerance, consistency at scale

 * Plan and build connection layers for EHR platforms and health APIs within isolated network environments

 * Own cloud infrastructure end-to-end. Build systems that are repeatable, observable, and easy to change

 * Drive engineering excellence: rigorous code and architecture reviews, testing strategies, and observability frameworks

 * Partner with Data Science, Product, and Business to turn ambiguous requirements into production-grade solutions


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