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About This Role
Overview: To be part of our organization, every employee should understand and share in the YNHHS Vision, support our Mission, and live our Values. These values - integrity, patient-centered, respect, accountability, and compassion - must guide what we do, as individuals and professionals, every day.
Support the design, development, and deployment of automation solutions for healthcare operations. Build Python-based services and workflow automations that improve clinical, operational, and financial processes, including integration with enterprise and EHR/EMR systems. Apply technical expertise in automation development, healthcare IT/interoperability, and agile delivery to provide scalable, secure, and reliable digital workflows under the guidance of the Automation Architect.
EEO/AA/Disability/Veteran Responsibilities:
- Collaborate with the Automation Architect and cross-functional stakeholders (clinical operations, finance, IT, analytics) to perform discovery, translate requirements into technical workflows, and prioritize high-ROI automations.
- Design, develop, and maintain Python scripts, services, and tools that automate clinical and operational workflows (revenue cycle, scheduling, reporting, user provisioning), including robust exception handling, logging, and retries.
- Develop and implement automation solutions using Automation Anywhere when appropriate and custom-built platforms (e.g., Django/YDA, Playwright), applying workflow orchestration, queues, schedulers, and asynchronous processing.
- Build and maintain scalable automation frameworks leveraging best-of-breed tools and patterns (e.g., Django Admin, Playwright, CouchDB, REST APIs), ensuring maintainability, testability, and reuse.
- Develop and support EHR/EMR integrations and interoperability workflows (e.g., Epic), including API-based integrations and HL7 v2 / FHIR interfaces; manage message ingest, mapping, validation, and routing.
- Build and support ETL/data pipelines between clinical systems, data platforms/warehouses, and downstream applications; ensure data quality, lineage, and reliable downstream consumption.
- Monitor automation executions and data pipelines; implement health checks, alerting, and observability; troubleshoot, debug, and optimize for performance, reliability, and scalability.
- Ensure compliance with HIPAA and organizational security policies, including access controls and secure data handling; support audits and regulatory reviews by providing logs, documentation, and evidence of controls.
- Provide support to internal users, including training on new automated workflows and assisting with adoption and change management. Participate in proof-of-concept initiatives and pilot programs to validate automation strategies; measure outcomes (time saved, error reduction, operational impact) and iterate.
- Create and maintain technical documentation (technical specs, flow diagrams, runbooks, SOPs) and contribute to operational procedures and support readiness. Seek mentorship and guidance from the Automation Architect and senior engineers; continuously improve engineering practices.
Qualifications:
EDUCATION
Bachelor's degree in Computer Science, Software Engineering, Information Systems, Health Informatics, Data Science/Statistics, or related field - or significant automation development experience in a healthcare production environment in lieu of a specific degree.
EXPERIENCE
- Minimum of three (3) years hands-on experience in software engineering, workflow automation, or digital transformation, ideally in healthcare or another regulated industry.
- Demonstrated success delivering production automations end-to-end (design, build, deploy, monitor, and optimize), including Python-based services/scripts and/or RPA-style workflows with strong exception handling, logging, retries, and operational support.
- Experience integrating with enterprise systems and EHR/EMR workflows (e.g., Epic) using vendor APIs and/or healthcare interoperability standards (HL7 v2, FHIR), and building ETL/data pipelines between operational systems and downstream data platforms.
- Strong collaboration skills with cross-functional teams and the ability to lead or contribute to proof-of-concept efforts while ensuring solutions meet privacy and security standards (e.g., HIPAA). Exceptional candidates with significant, relevant internship experience may also be considered.
LICENSURE
- Certifications preferred at the time of application: Lean Six Sigma White Belt Certified Scrum Developer (CSD)
- Certification required by the 6-month review: PCPP1 Certified Professional Python Programmer Level 1
- Additional certifications are preferred but not required. Relevant examples include Automation Anywhere, UiPath, or Blue Prism; cloud certifications (AWS/Azure/GCP); and healthcare IT/interoperability training or credentials (HL7, FHIR, HIPAA/privacy).
SPECIAL SKILLS
- Ability to develop and implement automation solutions across the software development lifecycle in regulated, large-scale environments.
- Strong Python and SQL skills; familiarity with RESTful APIs, microservices, and event-driven architecture; and experience with workflow orchestration (queues, schedulers, asynchronous jobs, retries, and observability).
- Working knowledge of healthcare data and interoperability concepts (HL7 v2, FHIR) and experience integrating automations with EHR systems (Epic) is highly desirable.
- Proficiency with CI/CD, Git-based development, testing practices, and containerization technologies (Docker; Kubernetes preferred) is a plus.
- Ability to create clear technical documentation (specs, runbooks, diagrams), support troubleshooting/root-cause analysis, and communicate effectively with non-technical stakeholders.
PHYSICAL DEMAND
On-call: Participates in a scheduled on-call rotation for production automations and integrations. Includes nights/weekends/holidays as scheduled; responds according to department SLOs and incident severity. Go-live support: Provides hyper care during planned cutovers and release windows, which may require after-hours and extended shifts, occasional onsite presence, and coordination of command-center/war-room activities, back-out readiness, and post-go-live stabilization.
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 Yale New Haven 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. Mid-level AI roles across all categories have a median of $193,200.
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.
Yale New Haven Health AI Hiring
Yale New Haven Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Stratford, CT, 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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