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About This Role
Marathon Health is a leading advanced primary care provider, partnering with employer and union plan sponsors to improve health for millions of Americans. With nationwide onsite, nearsite, and network health centers, and virtual primary care, Marathon delivers a value\-based model that enhances the healthcare experience for members and providers, while driving meaningful cost savings for plan sponsors. Marathon is proud to be certified as a Great Place to Work®, reflecting the company's commitment to building an inclusive, high\-trust culture where all employees can thrive. Learn more at marathon.health
ABOUT THE JOB
The Senior Staff Engineer is one of Marathon Health’s most senior individual contributors and serves as the engineering organization’s center of gravity for excellence in agent\-assisted software development. The Senior Staff Engineer combines hands\-on technical delivery with broad organizational influence, using coding agents and modern agentic engineering practices to ship better software, faster, and to raise the engineering bar across Marathon’s technology organization. This role partners with engineering and product leadership to shape both what gets built and how it gets built, drives architectural decisions that span team boundaries, defines reusable engineering patterns, and grows agentic and AI\-native engineering fluency across Engineering, Data, and Infrastructure. The Senior Staff Engineer operates with significant autonomy on initiatives that cross organizational lines and translates the rapidly evolving practice of building software with agents into patterns and habits Marathon’s engineers can apply in daily work.
ESSENTIAL DUTIES \& RESPONSIBILITIES
- Deliver cross\-cutting engineering work — platform components, refactors, integrations, and high\-leverage features — at a pace achievable only through disciplined use of coding agents (Claude Code, Codex, or comparable) and modern agentic engineering practices.
- Set the technical standard for the engineering organization. Define and codify what good agentic engineering looks like at Marathon, including spec\-driven development workflows, agent steering documents, custom Skills, MCP server patterns, subagent orchestration, and disciplined context engineering. Package these as reusable skills, plugins, templates, and internal documentation that other engineers can adopt without reinventing them.
- Drive architectural decisions that span Engineering, Data, and Infrastructure. Partner with the Principal Architect and engineering leadership on systems design problems that cross team boundaries, lead ARB\-level reviews, and bring strong fundamentals to API design, distributed systems, event\-driven patterns, observability, and security within a HIPAA\-regulated environment.
- Build internal agents, MCP integrations, and RAG\-backed applications where the return on investment is clear. Apply agent development frameworks pragmatically, with appropriate evaluation, guardrails, and cost discipline.
- Influence both what gets built and how it gets built. Partner directly with engineering leaders, product partners, and internal stakeholders to shape initiative scoping, sequencing, and tradeoff decisions. Bring product judgment and technical discernment upstream into the question of whether a problem warrants engineering investment — not just downstream into how the work is delivered.
- Mentor senior and staff\-level engineers across the organization. Lead pairing sessions, design reviews, and brown\-bags. Grow agentic and AI\-native engineering fluency across Engineering, Data, and Infrastructure through hands\-on collaboration and visible example.
- Operate as a healthcare\-aware engineer. Respect PHI handling, BAA requirements, audit trails, clinical safety considerations, and regulated change management, and help teams move efficiently within these requirements.
QUALIFICATIONS
- BS or MS in Computer Science or related field, or equivalent professional experience.
- 10\+ years of professional software engineering experience, with significant time at Senior, Staff, or Principal IC levels.
- Deep systems design and architecture expertise, including distributed systems, API design, event\-driven patterns, data flow, observability, and security. Agentic capabilities are leverage on top of strong fundamentals; we are not hiring for AI experience in lieu of engineering experience.
- Hands\-on experience across a modern engineering stack: cloud platforms (AWS, Azure, or GCP); strong proficiency in at least one of .NET, Python, or TypeScript and working fluency in the others; containers and Kubernetes (EKS, AKS, GKE) or comparable orchestrators; infrastructure\-as\-code (Terraform); CI/CD pipelines (GitHub Actions or comparable); modern observability tooling; and data platforms such as Snowflake and Kafka.
- Daily\-driver fluency with agentic engineering tooling. Routine use of a major coding agent (Claude Code, Codex, Cursor, or comparable) as a primary delivery vehicle, with direct experience managing context, orchestrating subagents, working with hooks, skills, and plugins, and recognizing the failure modes of long\-horizon agentic tasks. Working knowledge of MCP — both consuming servers and authoring them — and of how Skills, slash commands, and agent steering documents fit together as a context\-engineering toolkit.
- Disciplined experience with spec\-driven development applied to real production work.
- Practical exposure to at least one agent development SDK or framework (such as OpenAI Agent SDK, LangGraph, or comparable), sufficient to make sound build, buy, or skip decisions about agent solutions.
- Working knowledge of retrieval\-augmented generation (RAG) patterns, including retrieval design, grounding, and the tradeoffs between RAG, fine\-tuning, and longer\-context approaches.
- Demonstrated technical influence beyond individual contributions — patterns, libraries, frameworks, internal tooling, documentation, or mentorship that other engineers adopted and benefited from.
- Comfort with product\-adjacent work, including crisp problem framing, direct conversations with internal customers, MVP scoping, and confident decisions about whether a problem warrants engineering investment.
- Strong written and verbal communication, with the ability to explain technical tradeoffs to executives, clinicians, and junior engineers in the appropriate register for each.
DESIRED ATTRIBUTES
- Healthcare or other regulated\-industry experience (HIPAA, HITRUST, SOC 2\).
- Experience with data orchestration and transformation tooling (Airflow, dbt, Databricks, or comparable) and modern data engineering patterns.
- Experience evaluating LLM\-powered systems, including eval harnesses, hallucination and regression testing, and cost and latency monitoring.
- Track record of building and shipping internal frameworks, plugins, or skills that other engineers adopted at scale.
- Cloud platform certification (AWS, Azure, or GCP) at the professional or architect level.
- Discernment about what to build. Skeptical of agentic solutions when a simpler tool would do the job better, and able to recognize when an LLM call is the wrong answer.
- Bias toward delivery. Prioritizes shipping production\-ready solutions with a clear improvement path over pursuing exhaustive designs that fail to reach production.
- Influence without authority. Changes how others work by being unambiguously strong at the work and generous with the lessons learned.
- Comfort with ambiguity. Operates confidently where the established practices end and reports back with patterns the rest of the organization can use.
Pay Range: $170,000 \- $195,000/yr
*The actual offer may vary dependent upon geographic location and the candidate’s years of experience and/or skill level.*
*We are accepting applications for this position until a candidate has been selected. To apply to this position and learn more about open jobs at Marathon Health, visit our careers page.*
Salary Context
This $170K-$195K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Marathon 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($182K) sits 17% below the category median. Disclosed range: $170K to $195K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Marathon Health AI Hiring
Marathon Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Pearl City, HI, US. Compensation range: $195K - $195K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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