Staff Machine Learning Engineer

At MCG, we lead the healthcare community to deliver patient-focused care. We have a mission-driven team of talented physicians and technical experts developing our evidence-based content and innovating our products to accelerate improvements in healthcare. If you are driven to enhance the US healthcare system, MCG is eager to have you join our team. We cultivate a work environment that nurtures personal and professional growth, and this is a thrilling time to become a part of our organization. With dynamic roles that offer meaningful impact, you'll be able to fully realize your potential. Plus, you'll enjoy world-class benefits and the security, stability, and resources of our parent company, Hearst, with over 100 years of experience.

TheStaff Machine Learning Engineer is a senior individual contributor responsible for building and evolving the machine learning engineering capabilities that enable continuous improvement of MCG’s AI-powered clinical solutions. The role focuses on developing tooling and infrastructure for automated prompt tuning, content feedback loops, model benchmarking, and related ML experimentation and evaluation capabilities.  Working with Data Scientists, Clinicians, Software Engineers, and Product, this role translates model-quality and operational improvement opportunities into scalable technical solutions. The Staff ML Engineer operates with significant independence, serves as a technical authority within ML engineering, and influences how MCG evaluates, improves, and operationalizes AI-enabled clinical solutions.

You Will:

• Design, build, and evolve ML engineering tooling that accelerates continuous improvement of MCG’s AI-powered clinical solutions, including automated prompt tuning, content feedback loops, and model benchmarking.
• Develop scalable experimentation and evaluation infrastructure that enables rapid, reproducible comparison and optimization of models, prompts, and AI approaches.
• Partner with Data Science, Clinical Prompt Engineering, Software Engineering, and Product teams to translate model improvement opportunities into reliable technical solutions.
• Translate ambiguous ML and clinical AI requirements into technical approaches, defining appropriate methodologies, interfaces, and implementation plans.
• Investigate complex ML engineering problems and develop practical solutions when established approaches or frameworks are insufficient.
• Establish reusable engineering patterns, standards, and best practices that improve the quality, efficiency, and scalability of ML experimentation and continuous improvement.
• Provide technical guidance and mentorship to engineering and cross-functional partners, influencing outcomes through technical expertise and judgment without direct people-management responsibility.

What We're Looking For:

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.
• At least 6 years of relevant professional experience in machine learning engineering, software engineering, data science engineering, or a closely related technical discipline.
• Experience designing and implementing production-quality machine learning systems, tooling, or infrastructure is required.
• Experience with Python and modern software engineering practices.
• Demonstrated ability to independently solve complex technical problems and determine effective approaches within an ambiguous problem space.
• Demonstrated experience collaborating with Data Science, Engineering, Product, or other cross-functional technical teams.

Preferred Qualifications:

• Experience developing automated prompt optimization or prompt evaluation systems
• Experience building model benchmarking frameworks or evaluation infrastructure for large language models
• Experience developing feedback loops that incorporate human or expert evaluation into ML systems
• Experience with model observability, experimentation platforms, or ML operations
• Experience developing reusable ML engineering frameworks or standards adopted across multiple team
• Strong technical communication and collaboration skills, with the ability to influence decisions through expertise rather than positional authority
• Demonstrated ability to operate effectively in ambiguous, fast-moving environments while balancing technical rigor with delivery

Pay Range:  $154,000 - $216,000

Other compensation:  Bonus Eligible

Location: Seattle Preferred 

Perks & Benefits:

Remote Work/Hybrid Work

Travel expected 1-3 times per year for company-sponsored events

Medical, dental, vision, life, and disability insurance

401K retirement plan; flexible spending and health savings account

15 days of paid time off + additional front-loaded personal days

14 company-recognized holidays + paid volunteer days

up to 8 weeks of paid parental leave + 10 weeks of paid bonding leave

LGBTQ+ Health Services

Pet insurance

Check out more of our benefits here: https://www.mcg.com/about/careers/benefits/

All roles at MCG are expected to engage in occasional travel to participate in team or company-sponsored events for the purposes of connection and collaboration. 

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Note on interviews : We may use AI tools to audio-record and transcribe interviews for note-taking purposes. These are used only by our hiring team and do not make decisions on their own. Let us know if you prefer an alternative.

MCG is a leading healthcare organization dedicated to patient-focused care. We value our employees' unique differences and are an Equal Employment Opportunity (EEO) employer. Our diverse workforce helps us achieve our goal of providingthe right care to everyone. We welcome all qualified applicants without regard to race, religion, nationality, gender, sexual orientation, gender identity, age, marital status, veteran status, disability, pregnancy, parental status, genetic information, or political affiliation. We are committed to improving equity in healthcare and believe that a diverse workplace fosters curiosity, innovation, and business success. We are happy to provideaccommodationsforindividuals. Please let us know if you requireany support.