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About This Role
Our Mission:
Our mission is to SAVE AND IMPROVE LIVES BY EMPOWERING HEALTHCARE CONSUMERS. Come be part of remarkable.
Overview:
How you can make a differenceHealthEquity is expanding the use of Artificial Intelligence (AI), including generative and agentic, to improve member, client, and teammate experiences while maintaining trust, safety, and compliance. The Principal Responsible AI Governance and Compliance role is a senior technical governance role responsible for ensuring AI systems are designed, deployed, and operated in a manner that is secure, compliant, auditable, and aligned with HealthEquity’s Responsible AI principles.
In this role, you will translate Responsible AI policy and governance requirements into concrete technical controls, validation mechanisms, and continuous‑assurance practices that scale across internal and third‑party AI systems. Partnering with IT, Engineering, Product Development, Legal, Privacy, and internal Security teams, you will directly influence how AI is built and governed by embedding risk‑based guardrails, evaluation standards, and monitoring into engineering and platform workflows—enabling innovation while reducing regulatory, security, privacy, and operational risk.
This role is critical to making AI governance real, defensible, and operational, ensuring HealthEquity can confidently adopt advanced AI capabilities without compromising member trust or regulatory obligations. What you’ll be doing* Engineer and operationalize technical AI governance controls across the full AI lifecycle, translating Responsible AI requirements into testable, measurable, and auditable controls.
- Lead or support technical assurance reviews for AI use cases, validating that documented behaviors, risks, and mitigations align with observed system behavior.
- Design and standardize AI governance artifacts, such as model/system cards, evaluation summaries, architecture and data‑flow documentation, risk‑and‑control mappings, and approval evidence.
- Establish continuous assurance mechanisms for AI systems by defining monitoring signals, metrics, dashboards, and automated evidence collection.
- Partner with security, privacy, data governance, legal, product, and engineering teams to embed AI controls by design into development and platform workflows.
- Drive continuous improvement efforts by identifying opportunities for enhancing security governance, risk management, and compliance practices.
- Serve as a principal technical subject matter expert on end\-to\-end AI governance lifecycle, providing guidance and advice to senior management on best practices and emerging
- Other AI Gov activities assigned by leadership.
What you will need to be successfulTechnical Expertise* Strong understanding of AI/ML and GenAI systems, including LLMs, RAG architectures, agentic workflows, tool‑calling patterns, and human‑oversight mechanisms.
- Hands‑on experience with AI risk assessment and validation, including bias/fairness, robustness, hallucinations, drift, security threats, and misuse scenarios.
- Experience translating governance or regulatory expectations into engineering‑ready controls, quality gates, and acceptance criteria.
- Familiarity with AI governance and risk frameworks (e.g., NIST AI RMF, NIST CSF, SOC 2 principles) and applying them pragmatically to real systems.
- Comfort using tooling and automation (e.g., dashboards, evidence repositories, scripting, APIs, workflow tools) to reduce manual governance overhead.
Experience \& Background* Bachelor’s degree in Computer Science, Engineering, Data Science, Information Security, or a related field (or equivalent practical experience).
- 3\+ years of direct experience working with or governing AI/ML systems in production or near‑production environments.
- Experience supporting audit, regulatory, or client assurance efforts through high‑quality technical documentation and evidence.
General* Ability to clearly communicate complex technical risk and control concepts to engineering, security, privacy, legal, and executive stakeholders.
- Strong technical writing skills for model documentation, validation summaries, decision logs, and audit responses.
- Sound judgment in balancing risk rigor with engineering practicality in fast‑moving AI development environments.
- Proven ability to influence without authority and act as a trusted technical advisor in governance decisions.
*\#LI\-Remote**This is a remote position.*
Salary Range: $127000\.00 To $165000\.00 / year Benefits \& Perks:
The actual compensation offer is determined based on job\-related knowledge, education, skills, experience, and work location. This position will be eligible for performance\-based incentives as part of the total compensation package, in addition to a full range of benefits including:* Medical, dental, and vision
- HSA contribution and match
- Dependent care FSA match
- Uncapped paid time off
- Paid parental leave
- 401(k) match
- Personal and healthcare financial literacy programs
- Ongoing education \& tuition assistance
- Gym and fitness reimbursement
- Wellness program incentives
Onboarding \& Travel
This is a remote role, with an in\-person onboarding training component. New team members must participate in Trailhead, HealthEquity’s immersive onboarding experience Trailhead is designed to foster meaningful connections, support your integration into the organization, and equip you with a strong understanding of our business. Trailhead participation is a key expectation of this role. Trailhead is held onsite at our headquarters once per quarter. HealthEquity covers all required travel and accommodations.
This role may begin with a virtual, self\-paced onboarding experience, followed by a mandatory onsite Trailhead session at a later date.
HealthEquity is committed to providing reasonable accommodations to team members with qualifying disabilities. Should you be selected for this role and require an accommodation, we will put you in touch with our Benefits Team so you can begin the accommodation request process.
Why work with HealthEquity :
HealthEquity has a vision that *by 2030 we will make HSAs as wide\-spread and popular as retirement accounts.* We are passionate about providing a solution that allows American families to connect *health and wealth*. Join us and discover a work experience where the person is valued more than the position. Click here to learn more. You belong at HealthEquity!
HealthEquity, Inc. is an equal opportunity employer, and we are committed to being an employer where no matter your background or identity – you feel welcome and included. We ensure equal opportunity for all applicants and employees without regard to race, age, color, religion, sex, sexual orientation, gender identity, national origin, status as a qualified individual with a disability, veteran status, or other legally protected characteristics. HealthEquity is a drug\-free workplace. For more information about our EEO policy, or about HealthEquity’s applicant disability accommodation, drug\-free\-workplace, background check, and E\-Verify policies, please visit our Careers page.
HealthEquity uses Microsoft Copilot to transcribe screening interviews between candidates and their direct Talent Partner for note taking and interview summaries. By scheduling a screening interview with us, you consent to Microsoft Copilot’s AI technology recording and transcribing your interview with your Talent Partner. This information will be reviewed for accuracy and then used by HealthEquity to summarize the interview, ensure accuracy, and facilitate our hiring process. We take privacy seriously. You have the option to opt out. If you wish to opt out of this Microsoft Copilot transcription, please notify your Talent Partner in advance of the interview. If we do not receive an opt\-out request from you, we will assume that you consent to the use of Microsoft Copilot.
At HealthEquity, our goal is to save and improve lives by empowering healthcare consumers. This shared purpose inspires everything we do, including how we approach hiring. Our process is designed to get to know the real you: your skills, experiences, and potential to make a difference. We value honesty, originality, and the courage to do the right thing, even when it is not the easiest path. Showing up as your authentic self reflects these values and helps us build something truly remarkable together.
As AI is becoming a common tool throughout the application process, we want to be clear about its appropriate use at HealthEquity. Using AI to support resume writing, research, or interview preparation is perfectly acceptable, provided the content is accurate and genuinely represents your qualifications and skills. For other key parts of our interview process, however, it is important that the ideas, communication, and work you share reflect your own voice, experiences, and thinking. We ask that you participate in our live interviews and complete any assessments without AI assistance unless instructions explicitly indicate otherwise or a specific exception is discussed and approved in advance. This approach ensures fairness, celebrates your individuality, and allows your authentic perspective to shine. Behaviors that do not align with these guidelines may result in disqualification from the hiring process or termination of employment if later discovered. We appreciate your understanding and look forward to learning about the unique contributions only you can bring to HealthEquity.
HealthEquity is committed to your privacy as an applicant for employment. For information on our privacy policies and practices, please visit HealthEquity Privacy.
Salary Context
This $127K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At HealthEquity Inc., 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($146K) sits 32% below the category median. Disclosed range: $127K to $165K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
HealthEquity Inc. AI Hiring
HealthEquity Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $136K - $165K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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