Interested in this AI/ML Engineer role at HealthEquity Inc.?
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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 difference
The Manager, AI Search \& Visibility is responsible for increasing HealthEquity's discoverability, authority, and visibility across search engines, AI assistants, answer engines, and other digital research channels. This role develops the strategies, insights, and measurement frameworks that help HealthEquity become easier to find, understand, trust, and recommend.
This role will serve as a strategic owner for how HealthEquity appears across traditional search, AI\-powered discovery, and emerging digital decision\-making environments. The Manager will connect visibility efforts to business outcomes by improving qualified digital traffic, market authority, competitive share of voice, buyer journey engagement, and pipeline influence.
Working cross\-functionally with Content, Communications, Social, Product, Web, Commercial, Analytics, and other marketing stakeholders, the Manager identifies visibility opportunities, monitors competitive trends, and delivers strategic recommendations that strengthen HealthEquity's digital presence, market position, and credibility with buyers, partners, consultants, and customers. Reporting Structure and Work Setting
The Manager, AI Search \& Visibility reports to the Director of Content Marketing. This role typically performs remote.
What you’ll be doing AI Visibility \& Search Strategy* Own HealthEquity's strategy for improving visibility across search engines, AI assistants, answer engines, and emerging digital discovery platforms.
- Develop and maintain a strategic roadmap that improves HealthEquity's discoverability, brand authority, market relevance, and competitive positioning over time.
- Identify opportunities to increase HealthEquity's presence throughout buyer research journeys, category discovery, competitive evaluations, and decision\-making moments.
- Monitor changes in AI search, search behavior, digital discovery trends, and buyer research patterns; recommend actions that maintain HealthEquity's competitive position.
- Translate visibility opportunities into business recommendations that support qualified traffic, brand preference, pipeline influence, and customer acquisition.
Content, UX \& Technical Optimization* Partner with Content, Communications, Product, UX, Web, and Analytics teams to improve content structure, information architecture, machine readability, and discoverability.
- Identify content gaps, authority gaps, user experience challenges, technical barriers, and metadata or structured data opportunities that impact visibility.
- Recommend content, website, and authority\-building initiatives that strengthen HealthEquity's presence throughout key buyer journeys.
- Advise stakeholders on content formats, navigation, linking strategies, page structure, and digital experiences that improve visibility, engagement, AI retrieval, and conversion.
- Collaborate with technical and web partners to ensure SEO and AI visibility considerations are incorporated into website enhancements, launches, migrations, and content updates.
- Apply dynamic and programmatic optimization approaches — using templates, rules\-based logic, and automation to improve visibility, structure, and machine readability across large volumes of pages and sites simultaneously — rather than relying solely on manual, page\-by\-page optimization.
Authority \& Digital Presence* Strengthen HealthEquity's authority, credibility, and visibility across owned, earned, shared, and third\-party digital properties.
- Serve as a subject matter expert on competitive digital visibility, including how HealthEquity and competitors appear across search engines, AI assistants, answer engines, review platforms, publications, and industry conversations.
- Partner with Communications, PR, Social Media, Content, and Commercial stakeholders to increase the likelihood that HealthEquity content, expertise, and thought leadership are surfaced, cited, and recommended.
- Identify opportunities to expand HealthEquity's presence within industry conversations, expert communities, publications, review platforms, partner channels, and other influential digital ecosystems.
- Recommend strategies that increase citations, endorsements, reviews, mentions, backlinks, expert references, and other authority signals that contribute to discoverability and trust.
Analytics, Reporting \& Governance* Establish measurement frameworks and dashboards that track visibility, share of voice, AI citations, referral traffic, authority signals, competitive positioning, organic conversion, and pipeline influence where measurable.
- Analyze performance trends and translate insights into strategic recommendations for marketing stakeholders and business leaders.
- Create regular reporting for marketing leadership that communicates progress, risks, opportunities, competitive movement, and business impact.
- Define KPIs, establish best practices, and create scalable processes for ongoing AI visibility, search optimization, competitive monitoring, and digital authority building.
- Provide strategic recommendations to marketing leadership on emerging risks, market opportunities, buyer discovery behavior, and areas requiring investment or prioritization.
Additional Duties and Responsibilities* Other duties as assigned.
What you will need to be successful
Required* Bachelor's degree in Marketing, Communications, Business, Analytics, or related field, or equivalent experience.
- 7\+ years of experience in SEO, organic search, digital marketing, content strategy, web strategy, or related disciplines.
- Demonstrated success improving visibility, search performance, and digital discoverability for a B2B brand.
- Experience with enterprise SEO and analytics platforms such as Google Analytics, Google Search Console, SEMrush, Ahrefs, BrightEdge, Conductor, or similar tools.
- Strong understanding of technical SEO principles, information architecture, content optimization, search analytics, and website performance.
- Experience leveraging generative AI technologies to improve visibility, content effectiveness, competitive intelligence, and digital discovery.
- Demonstrated experience optimizing content and visibility at scale using dynamic, template\-driven, or automation\-based approaches across large numbers of pages or sites, distinct from manual single\-page optimization.
- Experience building dashboards, analyzing performance data, and translating insights into actionable recommendations.
- Proven ability to influence cross\-functional stakeholders and drive initiatives without direct authority.
- Exceptional communication, presentation, and strategic thinking skills.
Preferred* Experience improving visibility within AI\-powered search environments, answer engines, conversational search experiences, or emerging discovery platforms.
- Experience working with headless CMS platforms, code repositories (e.g., GitHub), and project management tools such as Azure DevOps or Monday.com to plan, track, and implement visibility and optimization initiatives in collaboration with engineering and web teams.
- Experience partnering with Content, Communications, UX, Web, Product, Commercial, and Analytics teams to improve discoverability and digital authority.
- Familiarity with structured data, semantic search, entity optimization, content retrieval systems, knowledge graphs, and AI\-driven search technologies.
- Experience translating emerging technologies into practical, scalable, and measurable business strategies.
\#LI\-Remote
Salary Range: $104500\.00 To $136000\.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, 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 $104K-$136K range is in the lower quartile 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 ($120K) sits 44% below the category median. Disclosed range: $104K to $136K.
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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