Senior Software Engineer, Data Products
Role details
Job location
Tech stack
Job description
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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
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Design, build, and own scalable data pipelines for real-world health data from diverse sources
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Architect and implement production-grade AI-driven services for high-scale analysis of medical data.
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Build and operate robust backend services, APIs, and data products end-to-end. You design it, you build it, you run it
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Tackle genuinely hard distributed systems problems: low-latency access patterns, fault tolerance, consistency at scale
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Plan and build connection layers for EHR platforms and health APIs within isolated network environments
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Own cloud infrastructure end-to-end. Build systems that are repeatable, observable, and easy to change
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Drive engineering excellence: rigorous code and architecture reviews, testing strategies, and observability frameworks
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Partner with Data Science, Product, and Business to turn ambiguous requirements into production-grade solutions
Requirements
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5+ years building production software systems; care deeply about coding craft
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Fluent in Python, MCP and high scale / high throughout data pipelines
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Strong grasp of distributed systems, software architecture, and engineering fundamentals
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Experienced in building complex data pipelines and cloud-native systems on AWS
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Think holistically about correctness, performance, security, and cost
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Self-directed, clear communicator, thrive in a fast-moving startup with high ownership
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Genuinely excited by the mission: applying great engineering to improve patient outcomes
EXTRA CREDIT
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Experience in building a Large Language Model (LLM) pipeline (e.g., Qwen, Llama)
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Experience working with medical or clinical data (EHR, FHIR, HL7, claims, or de-identified patient datasets)
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Familiarity with data standardization frameworks, data cataloguing, or metadata management
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Experience with HIPAA compliance, data privacy, or healthcare security requirements
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Exposure to ML pipelines, feature stores, or working with data science teams
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A bachelor's, master's, or PhD in Computer Science or a related quantitative field