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Staff Machine Learning Engineer

MLOps Full-time Permanent United States

Job details

$154,000 - $216,000 Salary
United States Eligibility
Staff Experience
Full-time Employment

About this role

Role overview As a senior individual contributor, you will build the engineering capabilities that help teams evaluate and improve AI-enabled clinical products. The role centers on reusable tooling and infrastructure for experimentation, prompt tuning, feedback, and model benchmarking, working closely with data science, clinical, software engineering, and product partners. You will operate independently and shape technical approaches without direct people-management responsibility.

Responsibilities - Design and evolve tools for prompt optimization, content feedback, and model benchmarking. - Build scalable infrastructure for reproducible experiments and comparisons of models, prompts, and other AI approaches. - Turn model-quality and clinical AI improvement opportunities into reliable technical solutions with cross-functional partners. - Clarify ambiguous requirements, choose suitable methods and interfaces, and plan implementation. - Investigate difficult engineering problems and establish reusable practices that improve experimentation quality and scale. - Provide technical guidance and mentorship to engineering and partner teams.

Requirements - Bachelor’s degree in a relevant technical discipline or equivalent practical experience. - Six or more years in machine learning engineering, software engineering, data science engineering, or a related field. - Experience building production-quality machine learning systems, tooling, or infrastructure. - Proficiency with Python and modern software engineering practices. - Ability to independently resolve complex, ambiguous problems and collaborate across technical and product teams.

Nice to have - Experience with automated prompt optimization or evaluation, language-model benchmarking, expert feedback loops, model observability, experimentation platforms, or MLOps. - Experience creating reusable ML engineering frameworks or standards; strong communication and influence across teams.

Benefits and work setup - Remote or hybrid work; Seattle is preferred. Occasional travel, typically one to three times a year. - Bonus eligible. Benefits include health, dental, vision, life and disability insurance; retirement and savings plans; paid time off and holidays; volunteer days; parental and bonding leave; LGBTQ+ health services; and pet insurance.

Skills detected in the listing

PythonMachine Learning
Detected Nov 6, 2026
Last verified Nov 6, 2026

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