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At Adaptive, we're Powering the Age of Immune Medicine. Our goal is to harness the power of the adaptive immune system to transform the way diseases are diagnosed and treated.
As an Adapter, you'll have the opportunity to make a difference in people's lives. With Adaptive, you'll create a career highlight through collaboration with bright, curious colleagues working at the apex of innovation and application.
It's time for your next chapter. Discover your story with Adaptive.
Position Overview
The Senior AI Governance Analyst supports Adaptive's Enterprise AI Program Office as a senior, adaptable individual contributor in a fast\-evolving enterprise AI function. Reporting to the Director, Enterprise AI Program Office, this role helps build and operate the practical mechanisms that make AI work visible, coordinated, governed, enabled, and connected to business outcomes.
This role is designed for a strong generalist operator who can work across business, technical, risk, compliance, and adoption conversations without becoming a bottleneck. The role is not expected to be the sole subject matter expert for AI policy, legal, privacy, security, quality, architecture, or engineering decisions; it is expected to identify the right accountable experts, coordinate the path forward, maintain clear records, and help make AI easier to use responsibly.
As the Enterprise AI Program Office matures, this role may flex across operating model design, governance and controls, platform/tool coordination, functional AI leader support, workforce enablement, portfolio visibility, decision support, communications, and special initiatives
Key Responsibilities and Essential Functions
- Support Enterprise AI Program Office work across assigned workstreams, business areas, functional stakeholder groups, governance backlog items, enablement activities, and special initiatives.
- Help shape, assess, and route AI opportunities by clarifying business value, user population, data sensitivity, workflow impact, autonomy level, vendor or tool status, regulated process exposure, technical dependencies, adoption needs, and support model.
- Maintain the enterprise AI portfolio and backlog view across business use cases, functional roadmaps, platform requests, AI tools, pilots, production candidates, adoption initiatives, and workforce enablement activities.
- Coordinate risk\-triggered governance and control activities with Legal, Privacy, Information Security, QA/Compliance, Enterprise Architecture, SWE, IT, data owners, business owners, and other accountable subject matter experts when those reviews are needed.
- Prepare and maintain practical operating artifacts such as intake records, decision logs, AI impact assessments, review packets, action logs, risk registers, exception records, implementation readiness checklists, service\-model documentation, and evidence records.
- Build and maintain reusable Enterprise AI Program Office assets, including service catalog content, intake guidance, assessment criteria, starter kits, tool\-selection guidance, playbooks, templates, FAQs, office\-hours materials, and training support materials.
- Support functional AI leaders and business stakeholders by gathering department needs, adoption barriers, tool feedback, success stories, recurring questions, and demand signals; synthesize patterns for program office and sponsor review.
- Coordinate readiness checkpoints with Forward Deployed Engineers, SWE, Enterprise Architecture, IT, data architecture, vendors, and platform partners so business requests have clear owners, dependencies, sequencing, and implementation paths.
- Track AI\-related issues, incidents, near misses, policy exceptions, duplicated efforts, unclear ownership, blocked handoffs, adoption friction, and lessons learned; help convert recurring patterns into improved guidance and processes.
- Prepare sponsor and executive reporting inputs, including portfolio dashboards, roadmap updates, operational metrics, adoption signals, risks, open decisions, RAID summaries, and recommended next actions.
- Continuously improve AI intake, opportunity assessment, prioritization, governance routing, pilot launch, scaling, functional engagement, service request, and benefit\-tracking processes as the program office matures.
- All other duties as assigned.
Position Requirements (Education, Experience, Other)
Required
- Bachelor's degree or equivalent combination of education and relevant work experience.
- 5\+ years of experience in program management, product operations, business analysis, technology governance, transformation, change management, technology risk/compliance, data governance, enterprise technology enablement, or a related cross\-functional operating role.
- Working knowledge of AI, generative AI, enterprise technology, software delivery, data, digital adoption, risk assessment, and control concepts sufficient to translate between business stakeholders, technical teams, and governance functions.
- Demonstrated ability to operate in ambiguous, fast\-moving environments where standards, tools, and processes are still being built.
- Strong facilitation, stakeholder engagement, synthesis, and written communication skills, including the ability to create clear executive\-ready materials from ambiguous inputs.
- Experience maintaining roadmaps, dashboards, action logs, decision logs, evidence records, operating cadences, templates, playbooks, or program reporting artifacts.
- Ability to independently manage multiple workstreams, requests, and stakeholders; follow up on dependencies; organize information cleanly; and escalate issues with sound judgment.
- Judgment to distinguish lightweight coordination from true risk\-triggered review, identify when accountable subject matter experts are needed, and avoid turning every activity into a formal review gate.
Preferred
- Experience supporting an AI center of excellence, enterprise program office, product operations team, digital transformation program, governance function, adoption program, GRC process, or regulated technology portfolio.
- Experience in healthcare, biotech, diagnostics, regulated software, life sciences, or enterprise environments with privacy, security, quality, compliance, or data\-governance expectations.
- Familiarity with responsible AI concepts, AI literacy programs, NIST AI RMF, ISO/IEC 42001, AI impact assessments, model or system cards, third\-party risk management, or AI use case lifecycle management.
- Experience using Jira, Confluence, SharePoint, Smartsheet, PowerPoint, Excel, BI/reporting tools, ServiceNow, GRC tools, or similar collaboration and program\-management platforms.
\#LI\-Remote
Compensation
Salary Range: $90,000 \- $135,000
Possible "other compensation" elements to include:
- equity grant
- bonus eligible
ALERT: Malicious groups posing as Adaptive employees have recently used fraudulent email aliases to extend employment offers, provide fake documents, and request sensitive personal and financial information. Legitimate Adaptive employment opportunities are initiated through our careers page and extended after multiple interviews with verified employees. Adaptive does not ask new hires to purchase anything out\-of\-pocket, including home office supplies and equipment.
Interested in this position, but don't meet all the requirements? Adaptive is committed to building diverse, equitable, and inclusive teams across our organization. Please consider applying even if your experience doesn't match all the qualifications; you may be the exact candidate we're searching for!
Adaptive is not currently sponsoring candidates requiring work authorization support for this position.
Adaptive's posted compensation information includes a base salary (or hourly rate) range and summary of other available total compensation. The base salary range represents a minimum\-to\-maximum salary (or hourly rate) available to candidates upon extension of offer. Base salary is thoughtfully considered upon offer and is determined through multiple evaluation checks throughout the interview process, including: a candidate's ability to meet minimum qualifications (skills/experience/education), a candidate's ability to thoughtfully address preferred qualifications, current market conditions, and internal pay equity. Listed base salary is exclusive of bonus, commission, equity, differential pay, benefits, and other incentives.
Adaptive's benefits at\-a\-glance.
Adaptive Biotechnologies is an Affirmative Action and Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against based on disability. Please refer the "Know Your Rights: Workplace Discrimination is Illegal" Poster for more information. If you'd like to view a copy of the company's affirmative action plan or policy statement, please email [email protected].
If you have a disability and you believe you need a reasonable accommodation to search for a job opening or to submit an online application, please e\-mail [email protected]. This email is created exclusively to assist disabled job seekers whose disability prevents them from being able to apply online. Only messages left for this purpose will be returned. Messages left for other purposes, such as following up on an application or technical issues not related to a disability, will not receive a response.
NOTE TO EMPLOYMENT AGENCIES: Adaptive Biotechnologies values our relationships with our Recruitment Partners and will only accept resumes from those partners who have active agreements with Adaptive. Adaptive Biotechnologies is not responsible for any fees related to resumes that are unsolicited or are received by any employee of Adaptive Biotechnologies who is not a member of the Human Resources team.
Salary Context
This $90K-$135K range is in the lower quartile 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 Adaptive Biotechnologies, 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($112K) sits 49% below the category median. Disclosed range: $90K to $135K.
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.
Adaptive Biotechnologies AI Hiring
Adaptive Biotechnologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $135K - $135K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% 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 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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