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About This Role
Workforce Classification:
Hybrid
Join Our Team: Do Meaningful Work and Improve People’s Lives
Our purpose, to improve customers’ lives by making healthcare work better, is far from ordinary. And so are our employees. Working at Premera means you have the opportunity to drive real change by transforming healthcare.
Premera is committed to being a workplace where people feel empowered to grow, innovate, and lead with purpose. By investing in our employees and fostering a culture of collaboration and continuous development, we’re able to better serve our customers. It’s this commitment that has earned us recognition as one of the best companies to work for. Learn more about our recent awards and recognitions as a greatest workplace.
Learn how Premera supports our members, customers and the communities that we serve through our Healthsource blog: https://healthsource.premera.com/ .
As an AI Engineer IV , you’ll provide technical leadership to a team building AI/ML solutions that tackle complex business problems—from early concept and experimentation through secure, scalable production deployment. You’ll help shape AI strategy, guide solution design, and lead the delivery of high‑impact capabilities across data pipelines, model development, and AI‑enabled services.
This role blends deep hands‑on engineering with leadership: you’ll drive prototypes into production, influence architecture decisions for multi‑faceted AI systems, mentor other engineers, and elevate best practices for responsible AI in a highly regulated environment.
This is a hybrid role, located on our campus in Mountlake Terrace, Washington.
What you’ll do:
- Architect and deliver AI systems end‑to‑end. Recommend and develop comprehensive systems and frameworks for AI applications and products, balancing speed to value with reliability and maintainability.
- Lead ideation and rapid validation. Pitch concepts to leadership, then lead the design and build of prototypes and minimum viable products to validate AI/ML solutions before scaling investment.
- Shape cloud architecture for complex AI platforms. Contribute to designing and implementing cloud architecture for large, multi‑faceted AI systems (compute, storage, security, deployment, monitoring).
- Enable robust data foundations. Assist in the design and scaffolding of scalable data pipelines that power advanced AI systems and enable model lifecycle improvements.
- Operationalize AI via services and APIs. Define specifications for low‑latency APIs/services that deploy models and integrate AI into enterprise applications.
- Build monitoring and reliability into production. Develop code and approaches for monitoring models and AI systems to ensure consistent performance, accuracy, and reliability in production.
- Driving engineering excellence. Participate in principled, agile‑like development practices; ensure peer review for all assigned work and conduct reviews for others as needed.
- Document with production discipline. Create and maintain thorough documentation aligned with team procedures, SDLC expectations, and corporate policies.
- Mentor and raise the bar. Guide AI engineers on AI/ML best practices and help grow team capability through feedback, coaching, and knowledge sharing.
- Influence strategy and roadmaps. Advise team and division leadership on enterprise AI strategy, AI technology roadmaps, and AI infrastructure direction.
- Partner with stakeholders and uphold governance. Collaborate with external stakeholders to conceptualize AI solutions that deliver business value while adhering to AI governance, best practices, and data quality/security standards.
- Provide thought leadership. Contribute to the broader AI community through best‑practice sharing, patterns, reusable frameworks, and technical leadership.
- Perform other duties as assigned.
What you’ll need:
Required Qualifications
- Bachelor’s degree in computer science, Information Systems, Statistics, Mathematics, or a related field, or equivalent experience .
- At a minimum, combined expertise of 8 \+ years of relevant experience in software development, including knowledge of the software development lifecycle and proficiency in multiple programming languages, and industry experience developing, deploying, and maintaining AI/ML systems.
Preferred Qualifications
- Experience building AI solutions using cloud platforms and services (e.g., Azure AI services or similar) in a highly regulated environment; healthcare experience preferred.
- Experience successfully productionizing AI models, including scalable pipelines and robust monitoring systems.
- Strong background developing deep learning models using modern frameworks (e.g., TensorFlow, PyTorch, MLX).
- Experience with software design patterns, microservices, distributed systems, and container orchestration.
- Knowledge of ethical AI practices (explainability, fairness, bias mitigation) and applying them in real solutions.
- Experience implementing advanced LLM solution patterns (e.g., retrieval‑augmented generation and structured reasoning approaches) and setting team best practices through reviews and standards.
Knowledge, Skills, and Abilities
- Proven experience developing deep learning architectures (e.g., transformers, CNNs, GANs, LSTMs, GNNs, autoencoders, diffusion models, NODEs).
- Proven ability to debug and optimize AI systems, including performance analysis and tuning approaches.
- Strong software engineering skills and the ability to build secure, stable systems at scale.
- Expertise in AI system design and deployment patterns (pipelines, low‑latency services, monitoring).
- Strong communication and stakeholder influence—able to explain technical tradeoffs clearly to non‑technical audiences.
- Mentorship and leadership mindset with a commitment to knowledge sharing and raising team capability.
Premera total rewards
Our comprehensive total rewards package provides support, resources, and opportunities to help employees thrive and grow. Our total rewards are more than a collection of perks, they're a reflection of our commitment to your health and well\-being. We offer a broad array of rewards including physical, financial, emotional, and community benefits, including:
- Medical, vision, and dental coverage with low employee premiums.
- Voluntary benefit offerings, including pet insurance for paw parents.
- Life and disability insurance.
- Retirement programs, including a 401K employer match and, believe it or not, a pension plan that is vested after 3 years of service.
- Wellness incentives with a wide range of mental well\-being resources for you and your dependents, including counseling services, stress management programs, and mindfulness programs, just to name a few.
- Generous paid time off to reenergize.
- Looking for continuing education? We have tuition assistance for both undergraduate and graduate degrees.
- Employee recognition program to celebrate anniversaries, team accomplishments, and more.
For our hybrid employees, our on\-campus model provides flexibility to create your own routine with access to on\-site resources, networking opportunities, and team engagement.
- Commuter perks make your trip to work less impactful on the environment and your wallet.
- Free convenient on\-site parking.
- Subsidized on\-campus cafes make lunchtime connections with colleagues fun and affordable.
- Participate in engaging on\-site activities such as health and wellness events, coffee connects, disaster preparedness fairs and more.
- Our complementary fitness \& well\-being center offers both in\-person and virtual workouts and nutritional counseling.
- Need a brain break? Challenge someone to a game of shuffleboard or ping pong while on campus.
Equal employment opportunity/affirmative action:
Premera is an equal opportunity/affirmative action employer. Premera seeks to attract and retain the most qualified individuals without regard to race, color, religion, sex, national origin, age, disability, marital status, veteran status, gender or gender identity, sexual orientation, genetic information or any other protected characteristic under applicable law.
If you need an accommodation to apply online for positions at Premera, please contact Premera Human Resources via email at careers@premera.com or via phone at 425\-918\-4785\.
The pay for this role will vary based on a range of factors including, but not limited to, a candidate’s geographic location, market conditions, and specific skills and experience.
The salary range for this role is posted below; we generally target up to and around the midpoint of the range.
National Plus Salary Range:
$150,300\.00 \- $270,500\.00
*\*National Plus salary range is used in higher cost of labor markets including Western Washington and Alaska* *.*
We’re happy to discuss compensation further during the interview because we believe that open communication leads to better outcomes for all. We’re committed to creating an environment where all employees are celebrated for their unique skills and contributions.
Salary Context
This $150K-$270K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Premera Blue Cross, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. Disclosed range: $150K to $270K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Premera Blue Cross AI Hiring
Premera Blue Cross has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Mountlake Terrace, WA, US. Compensation range: $270K - $270K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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