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About This Role
Who We Are:
At Synapse Health, we're streamlining the durable medical equipment (DME) process. We manage intake, documentation, routing, claims, billing, and patient support. Our model reshapes how DME is delivered and experienced.
Since 2016, with decades of industry and leadership experience, we've delivered tech\-based solutions that help our partners to modernize operations, improve coordination, and reduce administrative burdens. By taking on operational and financial complexity, we're redefining how DME works for providers, prescribers, and patients. We are proud to offer *work that matters, on a mission that matter**s*.
*Learn more at*SynapseHealth.com*and on*Synapse Health's LinkedIn*.*
What We Need:
The Director of Data Science and Analytics reports directly to the SVP of Data, Analytics, and AI. Our operations team processes tens of thousands of DME orders every day, often relying on individual judgment to make routing and vendor decisions in the moment. We've now processed millions of orders overall — and we're at an inflection point where that scale of data lets us build a real prediction engine to support and strengthen those decisions, not just react order by order.
We want to build differentiated technology here, not just adopt what's off the shelf, and we want to prove it: measuring the pre/post impact of introducing this technology into our operations supply chain — on customer experience, cost, and quality — so every improvement is grounded in evidence, not assumption. This role owns both the intelligence and the economic evaluation behind that engine, built around three core problems:
- Vendor matching — deciding which vendor fulfills each incoming order, optimizing for patient experience, delivery speed, and cost.
- Order routing — examining how orders move from creation to delivery to identify the most efficient path, and routing new orders to maximize that efficiency. This includes flagging orders at risk of delay based on historical patterns across the variables that actually drive outcomes — DME equipment type, geography, supplier, and processing team.
- Supply chain optimization — finding the root\-cause bottlenecks across in\-flow and out\-flow and quantifying the counterfactual: if we made this change, how many more orders would we have processed? Every recommendation comes with a number attached, not just a hunch.
These three problems anchor the roadmap today, but the mandate extends further — this role also owns our data science work in Revenue Cycle Management and Finance initiatives, including anomaly detection, and other domains as Synapse's data science footprint grows.
From there, phase two takes this into agentic AI — evolving the system from one that recommends analyzed actions to one that automates them directly across the supply chain. This is a player\-coach role: you'll be building models yourself while leading the team that builds the rest.
What You Will Do:
- Anchor the roadmap to the P\&L before writing a single model. Quantify the actual cost of a bad vendor match, a mis\-routed order, and network bottlenecks against our capitated rate — so every project you take on has a dollar figure attached before it starts.
- Break the roadmap into quarters, sequenced by leverage, not by ease. Turn vendor matching, order routing, and supply chain optimization into a quarter\-by\-quarter plan across the team— starting with whichever slice proves value fastest, then building toward the harder problems.
- Build the measurement infrastructure alongside the models, not after. Stand up the pre/post and counterfactual framework (holdouts, experiment design) as part of each build, so every recommendation ships with proof of impact on cost, quality, and customer experience — not a claim you have to retrofit later.
- Ship the first real win in your first 90 days. Pick the highest\-leverage, fastest\-to\-prove piece of the roadmap and get it live with a measured before/after result — this is what earns the team credibility to take on the bigger problems.
- Build the operating rhythm — the execution wheel. Put in place the sprint cadence, prioritization process, and delivery tracking that make the team's output predictable quarter over quarter, not just when you're personally driving it.
- Operate as a technical IC on design work — personally write and review technical design docs, document decisions clearly, and make that documentation visible across the team so the roadmap isn't dependent on any one person's tribal knowledge
- Build an early\-stage startup culture on the team. Set the tone for scrappiness, ownership, and speed — a team that ships and iterates, stays proactive in ambiguous situations, and moves work forward despite uncertainty.
- Lead and grow the team of data scientists. Hire, coach, and hold the team to a real delivery bar — while staying hands\-on to build models yourself, not just review.
- Own the data science roadmap across Operations, Revenue Cycle Management, and Finance initiatives like anomaly detection — ship the models, get them adopted, and iterate based on what the data shows post\-launch.
- Manage up in numbers. Report to the SVP — and the exec team when needed — in terms of dollars saved, orders recovered, and waste reduced, not narrative updates on "how the model is going."
- Partner with product on the roadmap. Make sure the data science roadmap and the product roadmap are pulling in the same direction — data science work should show up as capability the product can ship, not a parallel track.
- Push into phase two: agentic AI. Once vendor matching and routing are trusted and proven, evolve the system from recommending actions to automating them directly — architecting confidence thresholds and decision logic from day one so this transition doesn't require a rebuild.
- Partner with Engineering on the data foundation. Work with the Director of Engineering on Snowflake and the broader data architecture, so the platform can actually support what the team is building.
*Note: These responsibilities reflect the general nature and scope of the role but are not exhaustive. Responsibilities may evolve to meet changing business needs.*
What You Have:
At Synapse Health, we've intentionally built a culture rooted in kindness, collaboration, and creativity, qualities we consider essential for every team member. Additional requirements include:
- Education — Master's degree required in a quantitative field (Computer Science, Statistics, Data Science, Operations Research, or related)
- Experience — 8\+ years in data science, including 3\+ years directly managing a team of data scientists, including senior/staff\-level resources
- Prior experience at an early\-stage healthcare startup, with deep, hands\-on expertise in claims data and other healthcare data, including health outcome measurements
- Strong technical foundation in standard predictive ML (classification, regression, forecasting)
- Strong hands\-on proficiency in Python and SQL — able to write, debug, and optimize production\-quality code, not just prototype in a notebook
- Understands the full software development lifecycle and works fluently with GitHub — version control, branching strategies, pull requests, and code review — as a standard part of shipping models into production.
- Track record shipping ML products end to end, from experimentation through production, in close partnership with data engineering
- Able to write technical design docs, review the team's work at a high standard, and organize the team's structure around the roadmap
- Experience managing senior technical resources, not just junior ICs
- Demonstrate effective verbal and written communication skills, including presenting to executive stakeholders
- Demonstrate strong analytical and organizational skills, managing multiple workstreams and quarterly priorities broken into bi\-weekly delivery cadences
- Able to personally write, review, and document technical design docs — and make that documentation visible and accessible across the team, not siloed in your own head or a personal repo.
- Comfortable operating in a high\-pressure, ambiguous environment where priorities shift and requirements aren't always fully defined
What Sets You Apart:
Candidates are expected to have hands\-on experience in some — not necessarily all — of these areas, along with the ability to quickly learn new ones:
- Offline and online reinforcement learning for sequencing decisions that improve in\-flow/out\-flow over time
- Operations research methods (queueing theory / Little's Law, network flow optimization, discrete event simulation) applied to supply chain or logistics
- Rigorous causal inference skills — estimating heterogeneous treatment effects and applying quasi\-experimental designs like difference\-in\-differences and regression discontinuity
- Deep expertise in health economics — able to rigorously evaluate ROI and connect data science impact directly to business value
- Experience building agentic AI tools, with a point of view on how emerging AI capabilities could unlock future use cases beyond what's scoped today
What Sets Us Apart:
Work is a part of life, but at Synapse Health, we believe it should be meaningful and enjoyable. We're committed to helping our team members thrive personally and professionally, which is why our benefits include:
- Professional growth opportunities with compelling career paths
- Healthy work\-life balance supported by flexible paid time off (PTO)
- Comprehensive benefits package, including medical, dental, vision, STD \& LTD insurance for full\-time team members
- 401(k) savings plan with employer matching contributions
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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 Synapse 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. Director-level AI roles across all categories have a median of $272,150.
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.
Synapse Health AI Hiring
Synapse Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Skokie, IL, US.
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.
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