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About This Role
Why FMH
Every day, someone receives a life-changing diagnosis. Getting the right medication should be straightforward - but too often it isn’t.
Specialty medications move through a complex network of providers, health plans, pharmacies, and manufacturers. Administrative delays, disconnected systems, and limited visibility can slow treatment when time matters most.
Free Market Health exists to make that process work better.
Our technology connects stakeholders across the specialty pharmacy ecosystem, helping prescriptions reach the right pharmacy while improving visibility, reducing friction, and keeping treatment moving forward for the patients who depend on them. We focus on the most important stakeholder of all: the patient.
Your impact At Free Market Health, our mission is to never let the process fail the patient. We build the platform that connects payers and specialty pharmacies and routes specialty medication referrals, so patients get the therapy they need. We're looking for a senior engineer who wants to go deep on a part of our platform, become the go-to expert for it, and help shape how our team builds software with AI agents.
You'll own a business domain end to end: its services, data, integrations, and the decisions about where it goes next. You'll write a lot of code, work closely with the people who use what you build, and help other engineers be successful in your area.
What You’ll Do
- Design, build, and operate backend services, APIs, and integrations in Python, with frontend work in React when it's needed.
- Become the subject matter expert for a business domain, including referral workflows, pharmacy integrations, and the data that moves between them.
- Partner with Product and operations teams to understand problems and shape solutions.
- Review designs and code across teams that touch your domain.
- Build and improve the agents, skills, rules, and context that help engineers work in your domain with AI coding tools.
- Bring agentic development practices to the team: spec-driven work, delegating implementation to agents, and validating what they produce.
- Improve reliability and data quality in the systems you own.
What We’re Looking For
- 7+ years of professional software engineering experience, with deep backend experience in Python or a similar language.
- A track record of owning complex production systems and becoming the expert others rely on.
- Experience with system integrations, data pipelines, REST APIs, event-driven architecture, and relational databases such as SQL Server.
- Hands-on experience with agentic coding tools such as Claude Code or Cursor, including writing agent instructions, skills, or MCP integrations.
- Curiosity about the business, and a habit of asking why before building.
- Clear writing, and the instinct to encode what you know into tools other engineers can use.
Nice to Haves
- Healthcare technology experience, especially specialty pharmacy, PBM, payer, claims, or EDI integrations.
- Experience with Microsoft Azure, Azure Service Bus, Databricks, or Datadog.
- Experience with LLM application development or AI workflow automation.
- Familiarity with HIPAA, HITRUST, or SOC 2.
Life at FMH
We’re building more than a company. We’re building a team of ambitious, curious people who are passionate about solving meaningful problems, growing their careers, and genuinely enjoying the journey together.
Based in Pittsburgh’s Strip District, FMH draws inspiration from the city we call home. Our value of Grit isn’t just a word on a wall. It’s a mindset inspired by the city around us: showing up, rolling up our sleeves, and finding a way forward no matter the challenge.
As FMH continues to grow, so do the opportunities for our people to leave their mark. Guided by our core values of Integrity, Adaptability, Grit, and Collaboration, we foster an environment where trust is earned, new ideas are welcomed, challenges are embraced, and success is shared. You will be surrounded by people who care deeply about the work they do and the people they do it with.
Together, we’re building something special, and we’re just getting started.
Benefits
- Unlimited PTO
- 100% employer-paid individual medical, dental, and vision premiums
- Employer HSA contribution
- Paid parental leave
- 401(k) match
- Transportation and parking benefits
- Flexible, collaborative work environment
Benefit eligibility is based on employment status.
Build What’s Next With Us
Healthcare is evolving - and so are we.
If you’re excited by meaningful challenges, collaborative teammates, and the opportunity to help build a growing healthcare technology company, we’d love to hear from you.
Equal Opportunity Employer
Free Market Health is proud to be an Equal Opportunity Employer.
We believe that a diverse and inclusive workplace makes us better teammates and problem solvers and we encourage all qualified individuals to apply. Employment decisions are based on business needs, qualifications, and merit.
FMH is also committed to providing reasonable accommodations throughout the hiring process. If you require assistance or accommodation due to a disability, we will work with you to meet your needs.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Free Market 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Free Market Health AI Hiring
Free Market Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Pittsburgh, PA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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