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As a ML Engineer, you’ll lead the development of early-phase, high-impact ML systems; own the internal ML development environment, including instrumentation, benchmarking, and experimentation; and help bring scientific rigor into production environments so ideas move rapidly from research to validated pipelines.
You’ll partner closely with Research Scientists to make models production-ready with clear handoff contracts, performance gates, and packaging standards, and with ML Platform & Operations teams to ensure safe rollouts.
Build training and inference infrastructure for a fast-moving research team, including experiment tracking and benchmarking.
Design and develop scalable AI systems for retrieval, ranking, categorization, and generative AI over large-scale unstructured healthcare data.
Design, build, and operate AI/ML systems end-to-end, from problem framing and model selection to production build and deployment, through ongoing improvement.
Work backwards from complex business problems to define AI abstractions and system architectures that are scalable, explainable, and maintainable.
Bring rigor to scientific decisions by defining the right evaluation datasets, metrics, and validation strategies tied to real outcomes.
Design ranking and triage models that determine how work is routed between AI agents and human operators.
Establish feedback loops and data flywheels that continuously improve model performance in production.
Partner closely with software engineers, product managers, clinicians, and operators to ensure AI/ML systems deliver measurable business value.
Prototype and scale new AI capabilities from 0 → 1, then harden them for real-world production use.
Mentor other AI/ML engineers and promote best practices across modeling, evaluation, and deployment.
Minimum 5 years of software engineering experience.
Minimum 2 years of machine learning (ML) experience.
Experience building and operating AI/ML systems end-to-end, from problem framing and model selection through production deployment and continuous improvement.
Strong experience with ML training and inference infrastructure, including experiment tracking, instrumentation, and benchmarking.
Experience designing scalable AI systems for retrieval, ranking, categorization, and generative AI.
Experience working with large-scale unstructured healthcare data.
Strong understanding of model evaluation, datasets, metrics, validation strategies, and performance measurement.
Ability to design scalable, explainable, and maintainable AI abstractions and system architectures.
Experience developing ranking and triage models for AI-agent and human-operator workflows.
Experience taking AI/ML capabilities from prototype/0→1 development through production hardening.
Ability to collaborate effectively with Research Scientists, ML Platform & Operations, Software Engineers, Product Managers, Clinicians, and Operators.
Strong understanding of ML modeling, evaluation, deployment, and production best practices.
Experience mentoring other AI/ML engineers and promoting engineering and ML best practices.
Interested in this role?
Submit your application through our careers portal to be considered for this position.