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About This Role
VP, AI and Engineering (local candidates only)
We are looking for a VP, AI and Engineering to join the leadership team at HDAI. You will report to the Chief Product Officer and lead the ML Operations, API Services, Frontend Engineering, and Cloud Engineering teams. These teams build and operate the HealthVision™ platform, which ingests clinical data from our health system partners, runs AI and ML inference, and delivers insights back to clinicians at the point of care.
Our platform runs on AWS, combines predictive ML models with LLM\-powered clinical summarization, and serves results directly into EHR workflows. The challenge ahead is threefold: scale reliably as we onboard new health systems, deepen the AI product so clinicians get more value from every interaction, and do both while keeping cloud costs disciplined and maintaining the compliance posture (HIPAA, SOC 2, HITRUST) that our customers depend on. You will set the technical direction for how we tackle these problems and build the team and processes to execute against it.
A successful candidate has led engineering and applied AI teams in a regulated healthcare environment and knows what it takes to ship software that clinicians trust with patient care. You are as comfortable discussing model performance with a data scientist as you are reviewing an architecture decision with a cloud engineer or presenting a roadmap to a health system leader. At HDAI, we nurture a culture of continuous improvement, and one of the best assets of our leaders is a love for learning. We hope that you too share this goal. We are excited to have you as a part of this team!
Responsibilities:
- Build, lead, and develop a high\-performing engineering organization across multiple disciplines, setting clear goals, establishing operating rhythms, and creating meaningful career paths for the team.
- Own the technical vision and architecture of the HealthVision™ platform, making sound long\-term bets while delivering on near\-term commitments to customers and the business.
- Drive platform scalability and reliability as HDAI expands to new health system partners, ensuring that onboarding a new customer does not become a bespoke engineering project every time.
- Advance HDAI’s AI and LLM capabilities from their current production state into more powerful, cost\-efficient, and clinically impactful tools—working closely with clinical and product leadership to identify the highest\-value problems to solve next.
- Own cloud infrastructure strategy and cost management across HDAI’s AWS footprint, treating cost discipline as an engineering problem rather than an afterthought.
- Ensure that compliance (HIPAA, SOC 2, HITRUST) and data security are embedded in how the team designs and ships software, not bolted on after the fact.
- Partner with customer success leadership to support health system partnerships, including representing engineering in clinical and executive conversations with customers.
- Establish and evolve the engineering operating model: release management, quality assurance, incident response, on\-call, and post\-incident learning.
- Identify and implement leverage points—whether AI\-assisted tooling, process improvements, or smart automation—that allow a lean team to punch above its weight.
- Contribute to company strategy as a member of the executive team, bringing a grounded technical perspective to business decisions.
Qualifications:
- Bachelor’s degree in Computer Science, Engineering, or a related technical field. An advanced degree (MS, PhD, or MBA) is a plus but not required.
- 10\+ years of progressive engineering experience, with at least 4 years leading multi\-team engineering organizations (managing managers, not just ICs).
- Meaningful experience with applied AI/ML in production systems. Familiarity with modern LLM application patterns (retrieval\-augmented generation, structured extraction, prompt engineering at scale) is strongly preferred.
- Deep fluency with cloud\-native architectures on AWS. You should be able to evaluate trade\-offs in serverless vs. containerized designs, reason about event\-driven systems, and have opinions about infrastructure\-as\-code.
- Experience shipping production software in a regulated healthcare environment—you understand what HIPAA compliance means in practice, not just in policy documents. Familiarity with SOC 2, HITRUST, HL7/FHIR, or EHR integration (especially Epic) is a significant plus.
- A track record of improving platform economics and performance—whether that’s reducing cloud spend, improving system throughput, or standardizing messy data pipelines—at meaningful scale.
- Experience supporting enterprise customer relationships from the engineering side, including technical onboarding, integration planning, and executive\-level communication with health system leaders.
- Strong software engineering fundamentals. You may not write production code daily, but you have the judgment to set a high bar for quality and the credibility to hold the team to it.
- Excellent communication skills—written, verbal, and visual—with the ability to translate between clinical stakeholders, engineers, and business leaders.
- You exemplify strong accountability and ensure the quality of your work and your team.
Pay: $190,432\.00 \- $230,525\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Flexible schedule
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Referral program
- Retirement plan
- Tuition reimbursement
- Vision insurance
Work Location: Hybrid remote in Dedham, MA 02026
Salary Context
This $190K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Health Data Analytics Institute, 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 $181,170 based on 12,692 positions with disclosed compensation. This role's midpoint ($210K) sits 16% above the category median. Disclosed range: $190K to $230K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Health Data Analytics Institute AI Hiring
Health Data Analytics Institute has 2 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer. Based in Dedham, MA, US. Compensation range: $160K - $230K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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