Staff Machine Learning Engineer

Mcghealth; United States; Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Life insurance

Disability insurance

Retirement plan

Paid time off

Holidays

Volunteer days

Parental leave

Bonding leave

LGBTQ+ health services

Pet insurance

Job summary

Mcghealth is seeking a senior individual contributor to build engineering capabilities for evaluating and improving AI-enabled clinical products. You will focus 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, shape technical approaches, and not have direct people-management responsibility.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 6+ years in machine learning engineering, software/data science engineering, or related field.
• Experience building production-quality ML systems, tooling, or infrastructure.

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.

Skills

Python

ML engineering

Software engineering

Production systems

Education

Bachelor's degree

Job description

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.