Interested in this AI/ML Engineer role at MCG Health?
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About This Role
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.
MCG develops evidence-based guidelines that help patients get the right care in a variety of healthcare settings. The data science team at MCG combines AI with expert knowledge from our editors and guidelines to increase the efficiency and accuracy of our users' documentation workflows. We have years of experience applying NLP to the clinical domain and are expanding the boundary of what can be done with agentic workflows in a reliable, explainable way.
As a Principal ML Architect in MCG's Data Science team, you will shape how our agent-based products move from early concepts to scalable solutions. You will partner with Product Managers, Software Engineers, Data Scientists, clinical partners, and customers to turn emerging needs into practical architectures, prototypes, and clear paths to delivery. This role combines technical direction with hands-on discovery and customer engagement, helping demonstrate the value of MCG's agentic offerings and making adoption straightforward.
You Will
- Lead technical discovery with Product Managers and customers, translating emerging needs and workflows into end-to-end architectures for AI features.
- Set architectural direction for agent-based solutions, balancing near-term customer needs with scalable designs that can support future capabilities.
- Build prototypes and proof-of-concept experiences to test feasibility, demonstrate value, and gather feedback from clinical personnel and customers.
- Work directly with early-stage customers to understand their environments, identify adoption barriers, and shape requirements as solutions evolve.
- Partner with Software Engineers and Data Scientists to move solutions from concept and prototype into reliable, scalable products.
- Make architectural tradeoffs visible, identify risks early, and turn ambiguity into clear technical recommendations and execution plans.
- Communicate architecture decisions, constraints, and opportunities clearly across technical, clinical, product, and customer audiences.
Our Tech Stack
- Python, Go, C#
- Azure, AWS
What We're Looking For
- At least 10+ years of experience in a ML Engineer role or a related field.
- Demonstrated experience designing and shipping large-scale AI solutions with a focus on reliability and scalability.
- Strong architecture judgment, including the ability to anticipate future needs and make sound long-term design decisions.
- Experience working with early-stage customers to explore needs, shape requirements, and adapt solutions as new information emerges.
- Ability to work through ambiguity and create a practical technical direction and execution plan from incomplete requirements.
- Experience partnering across Product, Software Engineering, Data Science, clinical teams, and customer stakeholders.
- Excellent written and verbal communication skills, including the ability to explain complex architecture choices clearly.
Preferred Qualifications
- Experience with deep learning, natural language processing, or related AI methods.
- Experience working in healthcare or with medical data.
Pay Range:$183,000 - $257,000
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.
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 providing the 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 provide accommodations for individuals. Please let us know if you require any support.
Salary Context
This $183K-$257K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At MCG Health, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $183K to $257K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
MCG Health AI Hiring
MCG Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $257K - $257K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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