Interested in this AI/ML Engineer role at Devoted Health?
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Job Description
*A bit about this role:*
Devoted Health engages in millions of member interactions every year through a frontline organization of team members and AI agents spanning Member Service, Specialty Care Coordination, and Member Outreach, Sales and more. The quality of those interactions is the mission. Every call, every outreach, every touchpoint is a moment where we either earn or lose a member's trust. Getting this right at scale requires a fundamentally new approach.
You will build AI systems that monitor the quality of every member interaction and turn those signals into coaching and feedback that elevates both frontline team performance and frontline AI agent performance. You will define what high performance looks like across these teams and AI agents and build the systems that ensure we hit our goals. You will be a close thought partner to the leaders of our frontline teams, ensuring that every tool you build is adopted and helps them meaningfully improve performance. This is a new role, and you will shape what AI\-native quality and performance operations looks like for this organization.
*Your Responsibilities and Impact will include:*
- Embed with frontline teams and their leaders to understand what quality looks like in practice: how members are engaged, where interactions break down, and how coaching happens (or doesn't) today.
- Own the performance of our highest volume frontline AI agents, across voice and chat, and continue to build out those agents to drive higher performance.
- Build AI\-powered quality monitoring systems that move the needle on our most important business outcomes. Evaluate member interactions in real time across millions of touchpoints, moving beyond pass/fail scoring to nuanced assessment of what actually matters for member experience, safety, and outcomes.
- Build coaching workflows that surface actionable feedback to leaders at all levels immediately, making every interaction an opportunity to improve. Every key stakeholder should have an approach to action on results so that we’re getting better every day.
- Visualize interaction data, evaluation criteria, coaching signals, and performance trends so the right insight reaches the right person at the right time, from rep to team lead to senior leader.
- Scale what works. Turn successful systems into reusable frameworks that extend to new teams, interaction types, and channels. What works for Member Service should accelerate what you build for Specialty Care Coordination, Member Outreach, and our Clinician orgs.
- Partner closely with frontline team leaders across levels, coaching them on how to use the tools you build, ensuring adoption, and iterating in real time in response to their feedback to drive value.
- Oversee a small team of operators and agent builders that will drive performance across all frontline agents and teams
*Required skills and experience:*
- 7\+ years in high\-ownership operator roles: startup strategy, biz ops, consulting, or internal tooling PM. You have built and run systems that made organizations meaningfully more effective, and you move quickly to working prototypes on real data with real feedback.
- People leadership experience: you have directly managed, mentored, or grown individuals or teams, and you bring that lens to every cross\-functional partnership. You know how to build trust quickly, give feedback that lands, and create the conditions for people around you to do their best work.
- Fluency with modern AI tools and platforms, with an active practice of pushing their limits. You're adaptive and curious — willing to learn, teach, lead, and follow.
- Systems thinking: you move from solving a single issue to designing a framework that prevents an entire category of issues. You generalize your work and think in feedback loops, not isolated fixes.
- An instinct for quality in human interactions: you translate intuition about what a good member experience feels like into structured criteria an AI system can apply at scale. You care deeply about doing what's right for members and the teams serving them.
- A coaching and enabling mindset: you work with frontline managers and quality leads to ensure AI\-generated insights actually change behavior. You challenge assumptions, do what makes sense even when it's unconventional, and care more about outcomes than credit.
- Exceptional communication: you tailor your style to move teams to action, from frontline guides to executives. You're comfortable with ambiguity and take ownership of outcomes end to end.
*Salary range:* $157,500 \- $208,980 annually
The pay range listed for this position is the range the organization reasonably and in good faith expects to pay for this position at the time of the posting. Once the interview process begins, your talent partner will provide additional information on the compensation for the role, along with additional information on our total rewards package. The actual base salary offered will depend on a variety of factors, including the qualifications of the individual applicant for the position, years of relevant experience, specific and unique skills, level of education attained, certifications or other professional licenses held, and the location in which the applicant lives and/or from which they will be performing the job.
Our Total Rewards package includes:
- Employer sponsored health, dental and vision plan with low or no premium
- Generous paid time off
- $100 monthly mobile or internet stipend
- Stock options for all employees
- Bonus eligibility for all roles excluding Director and above; Commission eligibility for Sales roles
- Parental leave program
- 401K program
- And more....
- *Our total rewards package is for full time employees only. Intern and Contract positions are not eligible.*
Healthcare equality is at the center of Devoted’s mission to treat our members like family. We are committed to a diverse and vibrant workforce.
At Devoted Health, we’re on a mission to dramatically improve the health and well\-being of older Americans by caring for every person like family. That’s why we’re gathering smart, diverse, and big\-hearted people to create a new kind of all\-in\-one healthcare company — one that combines compassion, health insurance, clinical care, service, and technology \- to deliver a complete and integrated healthcare solution that delivers high quality care that everyone would want for someone they love. Founded in 2017, we've grown fast and now serve members across the United States. And we've just started. So join us on this mission!
Devoted is an equal opportunity employer. We are committed to a safe and supportive work environment in which all employees have the opportunity to participate and contribute to the success of the business. We value diversity and collaboration. Individuals are respected for their skills, experience, and unique perspectives. This commitment is embodied in Devoted’s Code of Conduct, our company values and the way we do business.
As an Equal Opportunity Employer, the Company does not discriminate on the basis of race, color, religion, sex, pregnancy status, marital status, national origin, disability, age, sexual orientation, veteran status, genetic information, gender identity, gender expression, or any other factor prohibited by law. Our management team is dedicated to this policy with respect to recruitment, hiring, placement, promotion, transfer, training, compensation, benefits, employee activities and general treatment during employment.
We have been made aware of instances of fraudulent job postings and/or fraudulent recruiting activity by individuals purporting to represent Devoted Health. These fraudulent schemes often seek monetary contributions or payments from job seekers (such as for “start up costs” or “equipment”), or seek to collect sensitive personal information. These job postings and offers are NOTauthorized by Devoted Health and Devoted is not responsible for fraudulent offers, personal information that you may have disclosed, or payments made to third parties purporting to represent Devoted. We have reported this matter and are cooperating with law enforcement agencies. Devoted Health will never ask for financial commitment or contribution from a candidate at any stage of the recruitment process.
Salary Context
This $157K-$208K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Devoted 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 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. Disclosed range: $157K to $208K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Devoted Health AI Hiring
Devoted Health has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $172K - $208K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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