Interested in this AI/ML Engineer role at athenahealth?
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Join us as we work to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all.
Position Summary
You will own the product direction for the AI Concierge, the front door to the AI agents and experiences that support our Customer Success organization. You will be responsible for the experience a practice has when they come to us for help getting onboarded, going live, and getting the most out of athenaOne, deciding what the Concierge handles on its own, what it routes to another agent, and when a person should step in. You will work closely with engineering and architecture to understand how these systems behave, where they connect, how they must evolve, and you will turn that understanding into requirements teams can build against. Staying close to customers is essential. You will engage with them often, put working solutions in their hands early, and iterate quickly on what you learn. This role reports to the Director of Product Management, Service Experience, and is a hybrid role based in Boston, MA.
About the Team
athenahealth operates one of the largest connected networks in healthcare, working with more than 160,000 providers and 117 million patients. Our mission is to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all. The Service Experience zone is a new product development team, and its job is the AI enablement of the Customer Success journey: the part of the mission customers feel most directly when they are getting onboarded, getting trained, going live, and getting help optimizing their use of athenaOne.
The AI Concierge is where that comes together. From the moment a customer joins the athena network, the Concierge understands what they need and either handles it, routes it to the right AI agent, or brings in a person. Getting that right is a hard product problem and a new one, and this role owns it. You will shape what a delightful, AI-enabled customer experience looks like for everyone who joins us.
You will not be starting from a blank page. Our first version is already built, and part of the job early on is understanding what's been built, shaping the vision for where it goes, and putting improvements in customers' hands. You will work alongside Experience Design and engineering partners, and you will be uniquely chartered with owning product direction and making quick decisions.
Essential Job Responsibilities
- Own the product direction for the AI Concierge and the agentic onboarding experiences behind it, in partnership with the Director of Product Management.
- Define what the Concierge handles on its own, what it routes to another agent, and when it brings in a person, and design the handoff so the customer is not repeating themselves.
- Decide what the Concierge needs to know and remember about a customer to be useful, and what context travels with a request when it moves to another agent or to a person.
- Define how the Concierge behaves when it is unsure or wrong, covering when it should decline to answer, how it stays grounded in verified information, and how a customer recovers from a bad answer without losing trust.
- Stay close to customers, getting in front of them regularly to understand their experience firsthand, putting working solutions in their hands early, and iterating quickly on what you learn.
- Partner with the Experience Design team on customer research, using it to find unmet needs and to test whether what we built actually works before we scale it.
- Develop a deep understanding of onboarding and go-live workflows, system interactions, integration points, and operational dependencies across athenaOne and our enterprise platforms.
- Partner with engineering and architecture to define problems, weigh solution options, identify constraints, and guide scope and sequencing.
- Turn customer, operational, regulatory, and business needs into clear requirements, acceptance criteria, and measurable outcomes.
- Establish how we measure whether the Concierge is working, covering task success, answer quality, handoff rates, and cost, and use that evidence to decide what to fix and what to expand.
- Prioritize work based on customer impact, technical feasibility, effort, risk, and business value.
- Coordinate dependencies across teams and keep delivery risks, technical decisions, and release readiness visible.
- Use AI tools to accelerate research, analysis, documentation, and planning, applying your own judgment to validate what they produce.
Additional Job Responsibilities
- Support iterative delivery through discovery, backlog refinement, sprint planning, and ongoing clarification.
- Communicate product recommendations, tradeoffs, sequencing rationale, and release status to technical and nontechnical audiences.
- Partner with go-to-market, implementation, support, and customer-facing teams on feature readiness and adoption.
- Lead customer discussions, user groups, and industry events to strengthen market and workflow understanding.
- Maintain system compliance by monitoring applicable government, regulatory, and standards requirements and translating them into roadmap and feature deliverables.
- Maintain product artifacts, decision records, requirements, and delivery context in Jira and Confluence.
Expected Education & Experience
- Bachelor's degree in Computer Science, Information Systems, Engineering, or Business, or equivalent practical experience.
- 5–10 years of experience in product management, technical product management, business analysis, or software delivery, working in an agile environment.
- Experience in healthcare or health IT required.
- Experience working closely with engineers and architects on system-level problems, with the ability to reason about integration dependencies, data movement, and how things fail.
- A track record of turning ambiguous problems into plans a team can execute, including the discipline to write acceptance criteria and say what is out of scope.
- Experience with AI or LLM-enabled products, conversational or agentic experiences, or customer onboarding and implementation workflows, with enough hands-on fluency with AI tools to judge what is feasible and to design around what these models cannot reliably do.
- Strong communication skills, and the analytical judgment to investigate a problem across several systems and teams and come back with a recommendation.
- Preferred: experience taking inherited or vendor-built work to a first customer release; experience defining where an AI agent acts on its own and where a person approves; familiarity with EHR data migration or practice configuration.
- Ability to travel up to 10-15% annually for client site visits, conferences, and trainings.
Expected Compensation
$121,000 - $207,000*The base salary range shown reflects the full range for this role from minimum to maximum. At athenahealth, base pay depends on multiple factors, including job-related experience,* relevant knowledge and skills, how your qualifications compare to others in similar roles, and geographical market rates. Base pay is only one part of our competitive Total Rewards package - depending on role eligibility, we offer both short and long-term incentives by way of an annual discretionary bonus plan, variable compensation plan, and equity plans.
About athenahealth
Our vision: In an industry that becomes more complex by the day, we stand for simplicity. We offer IT solutions and expert services that eliminate the daily hurdles preventing healthcare providers from focusing entirely on their patients — powered by our vision to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all.
Our company culture: Our talented employees — or athenistas, as we call ourselves — spark the innovation and passion needed to accomplish our vision. We are a diverse group of dreamers and do-ers with unique knowledge, expertise, backgrounds, and perspectives. We unite as mission-driven problem-solvers with a deep desire to achieve our vision and make our time here count. Our award-winning culture is built around shared values of inclusiveness, accountability, and support.
Our DEI commitment: Our vision of accessible, high-quality, and sustainable healthcare for all requires addressing the inequities that stand in the way. That's one reason we prioritize diversity, equity, and inclusion in every aspect of our business, from attracting and sustaining a diverse workforce to maintaining an inclusive environment for athenistas, our partners, customers and the communities where we work and serve.
What we can do for you:
Along with health and financial benefits, athenistas enjoy perks specific to each location, including commuter support, employee assistance programs, tuition assistance, employee resource groups, and collaborative workspaces — some offices even welcome dogs.
We also encourage a better work-life balance for athenistas with our flexibility. While we know in-office collaboration is critical to our vision, we recognize that not all work needs to be done within an office environment, full-time. With consistent communication and digital collaboration tools, athenahealth enables employees to find a balance that feels fulfilling and productive for each individual situation.
In addition to our traditional benefits and perks, we sponsor events throughout the year, including book clubs, external speakers, and hackathons. We provide athenistas with a company culture based on learning, the support of an engaged team, and an inclusive environment where all employees are valued.
Learn more about our culture and benefits here: athenahealth.com/careers
https://www.athenahealth.com/careers/equal-opportunity
Salary Context
This $121K-$207K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At athenahealth, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($164K) sits 23% below the category median. Disclosed range: $121K to $207K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
athenahealth AI Hiring
athenahealth has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, Boston, MA. Compensation range: $207K - $207K.
Location Context
AI roles in Boston pay a median of $215,000 across 323 tracked positions.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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