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About This Role
Job Purpose
The Director, Denials AI is responsible for leading the successful implementation, adoption, optimization, and ongoing support of the Denials AI platform. The Director, Denials AI serves as the primary bridge between Product, Analytics, Data, Operations, Client Delivery, and Support teams to ensure that the platform continues to evolve while delivering measurable client outcomes.
Duties \& Responsibilities
- Serve as the business owner and primary advocate for the Denials AI Workflow platform
- Lead implementation efforts for new Denials AI clients and existing client expansions
- Oversee solution design, workflow configuration, testing, training, go\-live, and stabilization activities
- Ensure successful integration of client data, workflows, and operational processes into the platform
- Drive implementation best practices, standards, templates, and governance
- Identify implementation risks and proactively develop mitigation strategies
- Establish scalable implementation methodologies to support continued growth
- Partner with Product, Operations, and Engineering teams to define product vision, priorities, and roadmap
- Gather feedback from implementations, operations teams, and clients to identify enhancement opportunities
- Build, mentor, and lead a team of implementation consultants and solution analysts
- Create a culture of accountability, innovation, ownership, and continuous learning
- Establish performance metrics, implementation of KPIs, support SLAs, and operational dashboards
- Drive continuous improvement initiatives across onboarding, support, and product adoption processes
- Develop team capabilities in technical troubleshooting, workflow optimization, and client consulting
- Act as the primary escalation point for complex implementation and support issues
- Facilitate alignment between business requirements and technical execution
- Ensure stakeholder transparency through executive reporting and regular status communications
- Oversee post\-go\-live client support and transition to steady\-state operations
- Partner with support and development teams to resolve high\-priority client concerns
- Other duties as assigned
- Use, protect and disclose patients’ protected health information (PHI) only in accordance with Health Insurance Portability and Accountability Act (HIPAA) standards
- Understand and comply with Information Security and HIPAA policies and procedures at all times
- Limit viewing of PHI to the absolute minimum as necessary to perform assigned duties
Qualifications
- Bachelor's degree in Computer Science, Information Systems, Healthcare Administration, Engineering, Data Analytics, or related field
- 7\+ years of experience in healthcare technology, software implementations, product management, or technical operations
- 3\+ years of experience leading and managing teams
- 3\+ years leading implementation, support, consulting, or product\-focused teams
- Experience as a Product Owner, Solution Owner, Technical Lead, or Implementation Leader
- Proven experience managing complex software implementations
- Comfortable presenting to executives, clients, and technical teams with the ability to communicate complex concepts clearly and effectively
- Strong SQL skills with the ability to write complex queries, analyze datasets to validate implementation results
- Knowledge of healthcare data transactions
- Knowledge of AWS Cloud technologies, AI/ ML\-enabled healthcare applications preferred
- Proficiency in Microsoft Office Suite
- Strong interpersonal skills, ability to communicate well at all levels of the organization
- Strong problem solving and creative skills and the ability to exercise sound judgment and make decisions based on accurate and timely analyses
- High level of integrity and dependability with a strong sense of urgency and results oriented
- Excellent written and verbal communication skills required
Working Conditions
- Willingness to work a flexible schedule and outside of normal business hours as needed
- Occasional travel to client sites may be required
- Must possess a smart\-phone or electronic device capable of downloading applications, for multifactor authentication and security purposes
- Physical Demands: While performing the duties of this job, the employee is occasionally required to move around the work area; Sit; perform manual tasks; operate tools and other office equipment such as computer, computer peripherals and telephones; extend arms; kneel; talk and hear
- Mental Demands: The employee must be able to follow directions, collaborate with others, and handle stress
- Work Environment: The noise level in the work environment is usually minimal
Med\-Metrix will not discriminate against any employee or applicant for employment because of race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), political affiliation, military service, veteran status, other non\-merit based factors, or any other characteristic protected by federal, state or local law.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Med-Metrix, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Med-Metrix AI Hiring
Med-Metrix has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Parsippany-Troy Hills, NJ, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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