Senior Machine Learning Engineer, Patient Health Intelligence

Worldwide Salaried Open

Job Description:

Longitudinal Patient Health Views - Design and build comprehensive, longitudinal representations of patient health by aggregating and normalizing data from electronic health records, lab results, genomic reports, and other clinical sources.
Agentic Workflows - Architect and implement personalized agentic AI workflows for interacting with patient health data and automating patient outreach (e.g., follow-up scheduling, result delivery, care gap identification).
Data Warehouse Integration - Integrate AI systems with the GeneDX data warehouse, ensuring reliable data pipelines, appropriate data governance, and performance access patterns.
Interface Design and Development - Collaborate with product and design teams to build intuitive, delightful user interfaces for clinicians and patients to explore and interact with health data.
Generative AI and NLP - Apply generative AI techniques for clinical information extraction, summarization, and conversational interfaces over health records.
Technical Leadership - Develop new methods and approaches for complex problems; mentor junior engineers; contribute to architectural decisions within the team.

Requirements:

8+ years of professional software engineering experience, with significant depth in AI/ML.
Python expertise - Advanced proficiency; this is your primary language.
Agentic AI systems - Hands-on experience building agentic workflows, autonomous agents, and/or conversational AI / chatbot systems.
Agentic coding - Expertise leveraging AI-assisted coding tools and workflows for rapid, high-quality development.
Electronic Health Data - Familiarity with EHR systems, medical records, HL7/FHIR standards, and healthcare data formats.
Clinical ontologies - Experience working with SNOMED-CT, ICD-10, LOINC, RxNorm, HPO, or similar clinical terminologies.
Generative AI - Proven experience applying LLMs and generative models for information extraction, summarization, and/or retrieval-augmented generation (RAG).
Data integration - Experience integrating with data warehouses and building reliable data pipelines.
Develop new methods and approaches for solving complex problems.
Be accountable for significant portions of projects with proven, consistent contributions.
Work on complex issues requiring in-depth evaluation and independent judgment.
Collaborate cross-functionally with product, design, clinical, and data teams.

Benefits:

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