Interested in this AI/ML Engineer role at RadNet?
Apply Now →About This Role
Job Description: Job description
As artificial intelligence rapidly transitions from departmental experimentation to mission-critical operational infrastructure, DeepHealth requires an experienced, forward-looking leader to orchestrate enterprise-wide AI adoption, governance, risk assurance, and cost management. The Associate Director, AI Program Office serves as the single accountable leader bridging software engineering, product operations, enterprise productivity, and regulatory compliance.
Operating within the Office of the CTO, you will direct the AI Program Office across all major dimensions of enterprise AI utilization from software engineering acceleration and workforce productivity to autonomous operations and domain-specific applications. In our regulated healthcare and medical imaging environment, you will institute the operational standards, policies, and technical controls necessary to ensure that AI adoption remains safe, compliant, cost-effective, and directly aligned with measurable business value.
Enterprise AI Governance, Policy & Technical Controls
- Architect, operationalize, and maintain the enterprise AI governance framework, establishing transparent decision gates, review workflows, and escalation pathways.
- Author and maintain the enterprise AI Acceptable Use Policy, supported by SOPs, data-handling guardrails, and role-based permissions.
- Translate policy into automated technical guardrails via API gateways, connector permissions, model endpoint access rules, and usage quotas.
- Monitor emerging frameworks and legal mandates (NIST AI RMF, ISO/IEC 42001, EU AI Act, HIPAA, FDA SaMD guidance) and adapt internal compliance controls accordingly.
Tool Sanctioning & Central Registry
- Own and curate the registry of sanctioned AI models, platforms, and API connectors across the organization.
- Manage intake and multi-stakeholder evaluation of newly requested AI tools and capabilities.
- Proactively identify, monitor, and mitigate unsanctioned “shadow AI” tools and integrations in partnership with Information Security.
- Maintain onboarding standards so approved tools move from request to production quickly and safely.
Risk Review, Safety & Assurance
- Run a structured risk-assessment intake for emerging AI use cases, internal prompts/fine-tunes, and third-party AI integrations.
- Maintain complete governance artifacts, including algorithmic impact assessments, model cards, audit logs, and exception approvals.
- Partner closely with: + Information Security + Legal + Privacy + Quality/Regulatory Affairs
- Validate control effectiveness, address audit findings, and support customer and regulatory inquiries.
AI FinOps, Token Budgets & Cost Optimization
- Define usage thresholds, token allocation quotas, and department-level spend caps across enterprise AI models and platforms.
- Build and oversee enterprise AI visibility reporting to monitor usage patterns, token efficiency, license utilization, and latency metrics.
- Design and operate an equitable cross-business-unit chargeback and showback mechanism, identifying consolidation and volume-discount opportunities.
Program Roadmap & Use Case Factory
- Spearhead enterprise-wide adoption programs for core foundational platforms and emerging cognitive tools.
- Prioritize use-case intake across clinical operations, corporate functions, and engineering to maintain a high-ROI implementation backlog.
- Define KPIs and measure quantifiable outcomes, reporting program health directly to the CTO and executive leadership.
Strategic Vendor & Partner Management
- Act as the primary relationship owner for technology partners, and emerging AI vendors.
- Negotiate enterprise agreements, data confidentiality addenda (BAAs, zero-data-retention terms), license tiers, and performance SLAs in coordination with Procurement and Legal.
- Manage contract renewals and performance reviews to ensure ongoing value and compliance.
Enablement, Training & AI Culture
- Drive enterprise AI fluency through tailored role-based curricula, prompt-engineering playbooks, and interactive office hours.
- Serve as an internal consultant for department heads and product teams, turning ambitious business ideas into practical, compliant AI implementations.
- Promote a culture of responsible AI adoption in partnership with change management and communications teams.
PLEASE NOTE: This is not an exhaustive list of all duties, responsibilities and requirements of the position described above. Other functions may be assigned and management retains the right to add or change duties at any time.
Job requirements
Minimum Qualifications, Education and Experience
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, Health Informatics, Information Systems, Business Administration, or a related field (Master’s or MBA preferred).
- 8+ years of progressive leadership in technology program management, technology strategy, enterprise architecture, or IT risk/compliance, including 3+ years driving enterprise-wide technical transformation programs.
- Proven track record operationalizing technology, data, or AI governance programs within regulated environments (Healthcare, Life Sciences, MedTech, or Financial Services).
- Deep understanding of modern generative AI, agentic frameworks, LLM architectures, and associated risks (data leakage, IP exposure, bias, hallucination, non-deterministic behaviors).
- Familiarity with API gateways, identity and access management (IAM), data loss prevention (DLP), and cloud network access policies.
- Exceptional communication skills with demonstrated success aligning cross-functional executives across Engineering, Clinical Operations, Legal, Compliance, and Finance.
Preferred Qualifications
- Direct background in digital health, radiology/medical imaging, or medical software environments subject to FDA (SaMD), HIPAA, SOC 2, or GxP regulations.
- Hands-on experience establishing AI FinOps, token consumption governance, or showback/chargeback models across multiple subsidiaries or business units.
- Relevant certifications such as IAPP Certified Artificial Intelligence Governance Professional (AIGP), CIPP, CISM, PMP, or SAFe Agilist.
- Direct experience negotiating enterprise AI software agreements and vendor terms.
Quality Standards
- Communicates, cooperates, and consistently functions professionally and harmoniously with all levels of supervision, co-workers, visitors, and vendors.
- Demonstrates initiative, personal awareness, professionalism and integrity, and exercises confidentiality in all areas of performance.
- Follows all local, regional and country laws concerning employment.
- Follows all DeepHealth policies and procedures.
- Follows data privacy, compliance, safety and confidentiality standards at all times.
- Practices universal safety precautions.
- Promotes good public relations on the phone and in person.
- Adapts and is willing to learn new tasks, methods, and systems.
- Reports to work regularly as scheduled; consistently punctual with respect to working hours, meal and rest breaks, and maintains satisfactory personal attendance in accordance with DeepHealth guidelines.
- Completes job responsibilities in a quality and timely manner.
Travel
This position requires domestic/international travel up to 15%.
Working Environment
Remote / Hybrid
Pay Range: USD $170.00 - USD $190.00 /Yr.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At RadNet, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
RadNet AI Hiring
RadNet has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Somerville, MA, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.