Interested in this AI/ML Engineer role at Zelis Healthcare?
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About This Role
Description
At Zelis, we Get Stuff Done. So, let’s get to it!
A Little About Us
Zelis is modernizing the healthcare financial experience across payers, providers, and healthcare consumers. We serve more than 750 payers, including the top five national health plans, regional health plans, TPAs and millions of healthcare providers and consumers across our platform of solutions. Zelis sees across the system to identify, optimize, and solve problems holistically with technology built by healthcare experts – driving real, measurable results for clients.
At Zelis, AI is woven into the fabric of how we work. Every associate is expected - and empowered - to partner with AI to challenge the status quo, accelerate innovation, and amplify their impact. This is a place for builders with a growth mindset who act with agility, embrace change, and use modern technology to shape smarter solutions, exceptional experiences, and the future of our industry for our clients, customers, and our culture.
A Little About You
You bring a unique blend of personality and professional expertise to your work, inspiring others with your passion and dedication. Your career is a testament to your diverse experiences, community involvement, and the valuable lessons you've learned along the way. You are more than just your resume; you are a reflection of your achievements, the knowledge you've gained, and the personal interests that shape who you are.
Position Overview
Why Zelis?
Healthcare is one of the most complex industries in the world—and one of the biggest opportunities for AI to create meaningful impact.
Zelis sits at the center of the healthcare financial ecosystem, connecting payers, providers, and millions of healthcare consumers. We’re investing in AI not simply as a productivity tool, but as a new way to build technology and solve complex business problems.
At the center of that work is Apollo, our internal agentic AI platform. Apollo gives engineers the infrastructure, tools, controls, and reusable capabilities they need to safely build and deploy AI-powered applications across Zelis.
We’re looking for a hands-on AI Platform Engineer to help build it. You’ll join while the platform is still taking shape and have meaningful influence over the technical foundations other engineers will build on.
What You’ll Do
This role sits at the intersection of AI, platform engineering, developer experience, and enterprise-scale delivery. You’ll help turn rapidly evolving AI capabilities into dependable platform primitives that engineers across Zelis can use to build real applications.
You’ll have significant influence over how Apollo evolves—from the agent harness and execution environment to model and tool governance, context, observability, evaluation, and developer experience.
You will:
- Build the core agentic platform. Design and evolve capabilities across agent execution, workflow state and orchestration, context assembly, model routing, tool contracts, evaluation, observability, policy enforcement, and automated repair loops.
- Create safe execution environments. Build and operate isolated agent runtimes across containerized, local, and simulation environments, supporting persistent sessions, testing, previews, adversarial review, and diagnose-and-repair workflows.
- Develop reusable platform services. Help shape capabilities such as our AI Gateway, Context Engine, policy profiles, semantic models, project bootstrap tools, observability, and delivery integrations.
- Make thoughtful technology bets. Evaluate emerging foundation models, coding agents, MCP technologies, workflow engines, developer tools, and commercial and open-source platforms. Run disciplined experiments and turn the right ones into supported Apollo capabilities.
- Make security and governance part of the platform. Build identity-aware access, isolation, policy-as-code, auditability, and controls for model and tool usage so engineers can move quickly without creating unnecessary risk.
- Design for developers. Create clear APIs, interfaces, documentation, and paved paths that allow application engineers to take advantage of sophisticated AI infrastructure without needing to understand every element of the underlying control plane.
- Learn from real-world use. Work closely with forward-deployed/agentic AI and business-unit engineers, understand what is working and where they are getting stuck, and turn repeatable patterns into reusable platform capabilities.
What You’ll Bring
We care less about matching every technology on a checklist and more about finding someone who has built complex systems, thinks clearly about tradeoffs, and is energized by the opportunity to help define how AI engineering should work at scale.
You’ll likely bring:
- Strong software or platform engineering experience, including building and operating production systems used by other engineers.
- Experience with distributed systems, AWS and cloud infrastructure, container orchestration, CI/CD, infrastructure as code, APIs, and observability.
- Hands-on experience building with modern AI technologies such as foundation models, AI gateways, agent frameworks or harnesses, model evaluation, MCP, retrieval/context systems, or safe execution environments.
- Strong programming skills in Python, TypeScript, Java, Go, or another modern language, with the ability to move comfortably between application and platform code.
- A practical understanding of model behavior, evaluation, prompt and tool design, token and latency tradeoffs, and model-provider integration.
- Experience assessing new technologies with an eye toward security, reliability, maturity, cost, maintainability, and long-term platform value—not simply technical novelty.
- An understanding of identity, secrets management, network boundaries, audit logging, and policy enforcement in enterprise environments.
- The ability to work through ambiguity, run focused experiments, make clear technical decisions, and adjust course based on what you learn in production.
- Strong communication skills and an interest in working directly with engineers, product partners, security, architecture, and senior stakeholders to drive adoption.
- Experience building an internal developer platform, AI or agent platform, or engineering productivity product is especially relevant. Experience operating in healthcare, financial services, or another highly regulated environment is helpful but not required.
Please note at this time we are unable to proceed with candidates who require visa sponsorship now or in the future.
Location and Workplace Flexibility
Zelis is headquartered in the U.S., with multiple locations across the country and in Hyderabad, India. Check out our locations to learn more about our offices. All employee work locations are based on the needs of the position and are determined by the Leadership team. In-office work and activities vary based on work and team objectives in accordance with Company policies.
While location expectations vary by role, candidates within approximately 50 miles of a U.S. office are generally preferred to support collaboration when needed. Our hybrid approach is flexible, and in-office presence is guided by team and business needs rather than a fixed weekly schedule.
Base Salary Range
$180,000.00 - $247,500.00 At Zelis we are committed to providing fair and equitable compensation packages. The base salary range allows us to make an offer that considers multiple individualized factors, including experience, education, qualifications, as well as job-related and industry-related knowledge and skills, etc. Base pay is just one part of our Total Rewards package, which may also include discretionary bonus plans, commissions, or other incentives depending on the role.
Zelis’ full-time associates are eligible for a highly competitive benefits package as well, which demonstrates our commitment to our employees’ health, well-being, and financial protection. The US-based benefits include a 401k plan with employer match, flexible paid time off, holidays, parental leaves, life and disability insurance, and health benefits including medical, dental, vision, and prescription drug coverage.
Equal Employment Opportunity
Zelis is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
We welcome applicants from all backgrounds and encourage you to apply even if you don’t meet 100% of the qualifications for the role. We believe in the value of diverse perspectives and experiences and are committed to building an inclusive workplace for all.
Accessibility Support
We are dedicated to ensuring our application process is accessible to all candidates. If you are a qualified individual with a disability or a disabled veteran and require a reasonable accommodation with any part of the application and/or interview process, please email [email protected].
Disclaimer
The above statements are intended to describe the general nature and level of work being performed by people assigned to this classification. They are not to be construed as an exhaustive list of all responsibilities, duties, and skills required of personnel so classified. All personnel may be required to perform duties outside of their normal responsibilities, duties, and skills from time to time.
Salary Context
This $180K-$247K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Zelis Healthcare, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $180K to $247K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Zelis Healthcare AI Hiring
Zelis Healthcare has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $224K - $247K.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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