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About This Role
Position Description
The Senior AI/ML Engineer is responsible for building and operating the foundation that puts AI to work across the organization: the Azure infrastructure AI workloads run on, the patterns every engineering team uses to develop with agents, and UNOS’s own production agents.
UNOS will be building everything from conventional machine learning models to generative AI workflows to more autonomous agentic solutions; this role makes them run in production, safe for protected health information by default, with evaluation, monitoring, and cost under control.
This role requires a strong background in cloud /infrastructure engineering and the ability to apply that expertise to AI technologies while continuing to advance AI capabilities across UNOS. This role provides an opportunity to shape the trajectory of AI and agentic development and make a real difference in people’s lives, in a domain spanning healthcare, logistics, clinical science, and biotech.
Key Responsibilities
- Provision AI infrastructure on Azure as code, including model deployments in Microsoft Foundry and Databricks, GPU capacity, agent runtimes, as well as the networking, identity, and secrets around them.
- Stand up open-weight and frontier models on UNOS infrastructure under a pattern approved by Security and Privacy for protected health information (PHI).
- Develop the technical policies and build controls into the platform itself: per-agent identities, least-privilege access, private endpoints, allow-listed models, and audit evidence produced as a byproduct of normal use.
- Run shared model access across providers, with routing, quotas, tracing, and cost attributed to each team and use case.
- Build sandboxed environments where engineers run coding agents against real repositories, with no production credentials inside.
- Define UNOS’s standard patterns for agentic development and publish them as working templates and reference implementations.
- Integrate AI into CI/CD for pull-request review, test-failure triage, and security findings.
- Build and ship UNOS’s own agents, in UNOS One products and for internal operations, including tool access, retrieval, orchestration, and human approval where a decision requires it.
- Build the path from prototype to production in UNOS One, so a working proof of concept ships without being rebuilt.
- Own evaluation and release for agents and conventional models alike, including test sets, staged rollout, rollback, and monitoring.
- Baseline and track the time from idea to working proof of concept, and the quality of what ships.
- Teach agentic development across UNOS through pairing, working sessions, and code review, and mentoring engineers in the AI function.
- Recommend build-or-buy decisions on AI platforms and tools, with cost and risk stated.
- Perform other duties and support additional administrative priorities as assigned, consistent with the general scope and level of the position.
Minimum Requirements
- Approximately six (6) years of experience building and running production systems, including at least three (3) years owning cloud infrastructure as code.
Critical Skills
- Production cloud depth, Azure preferred (equivalent AWS or GCP experience considered), with Terraform or Bicep: virtual networks, private endpoints, managed identities, role-based access, secrets, and policy.
- Strong handle on fundamental principles of AI/ML (e.g. train/test/validation, overfitting, calibration, eval methods)
- Hands-on provisioning of AI workloads, including model serving endpoints, quota and capacity, GPU compute, and cost controls.
- An LLM-based system built and run in production, past the demo stage, including tool calling or agents, retrieval, evaluation, and tracing.
- Experience working under HIPAA, HITRUST, SOC 2, FedRAMP, or a similar control framework, translating controls into configuration and evidence.
- Daily practice with coding agents such as Claude Code, GitHub Copilot, Codex, or Cursor.
- Strong proficiency in Python, containers, and Kubernetes or an equivalent orchestrator.
- Demonstrated record of changing how a team works without managing it, through establishing useful repeatable patterns that reduce team burden and shorten time to solution or insight.
- Ability to choose what to build first with incomplete information and ship working versions early.
Additional Skills & Qualifications
- Experience with Microsoft Foundry, Foundry Agent Service, Azure OpenAI, or Azure API Management as an AI gateway.
- Experience with Databricks, including Unity Catalog, Model Serving, and MLflow.
- Experience serving open-weight models with vLLM, SGLang, or similar, including quantization and GPU capacity planning.
- Experience building MCP servers or other tool interfaces over existing APIs and legacy systems.
- Experience with agent security, including prompt-injection defenses, tool permissions, and sandboxing.
- Experience operating conventional machine learning: training pipelines, model registries, and drift monitoring.
- Experience with HL7 FHIR or EHR integration; organ donation and transplant experience is a plus.
Education
- Bachelor's Degree in Computer Science,Engineering or a related field preferred; equivalent experience considered.
Physical Requirements
- General office demands + Prolonged periods of sitting at a desk and working on a computer. + Frequent reaching, handling, and fine manipulation for using office equipment, filing, and managing paperwork. + Manual dexterity sufficient to operate a keyboard, mouse, and other office tools. + Occasional standing, walking, and bending. + Ability to lift up to 10-20 pounds occasionally. + Vision abilities required include close vision for computer work and reading documents. + Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Salary Context
This $135K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At United Network For Organ Sharing, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($145K) sits 32% below the category median. Disclosed range: $135K to $155K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
United Network For Organ Sharing AI Hiring
United Network For Organ Sharing has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in VA, US. Compensation range: $155K - $155K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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