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About This Role
About Compass Behavioral Group
Compass Behavioral Group is one of Virginia’s largest and most respected providers of behavioral and mental health services. We deliver compassionate, evidence\-based care to children, adolescents, and their families, and we invest deeply in the development of the clinicians who serve them.
Position Summary
Compass Behavioral Group is seeking a practical, people\-first AI Implementation \& Adoption Specialist to help our clinicians, administrators, and business teams become more effective in how they use artificial intelligence across every area of our organization.
This is not a heavy machine\-learning engineering role. It is a hands\-on, applied role for a change\-agent who loves solving real workflow problems, teaching others, and translating emerging AI capabilities into measurable value for a behavioral health organization. You will be the person who helps a therapist cut hours off documentation, helps intake teams route urgent inquiries through established clinical protocols, helps billing reduce denials, and helps leadership make smarter decisions with better data.
You will identify high\-value AI opportunities, pilot and implement approved tools, build reusable prompts and standard operating procedures (SOPs), train staff at every level, and partner with compliance and IT to ensure every solution is HIPAA\-safe, ethically sound, and never replaces human clinical judgment. You will work across clinical, administrative, billing, HR, compliance, intake/admissions, scheduling, and leadership workflows.
If you are excited by the idea of making a behavioral health organization more efficient and effective by putting practical AI into the hands of the people who serve clients, this role is for you.
What You Will Do (Key Responsibilities)
- Discover and prioritize opportunities. Partner with clinicians, BCBAs, licensed professionals, therapists, supervisors, intake staff, billing, HR, and leadership to understand operational pain points and identify where AI can save time, reduce manual effort, improve quality, and unlock better ways of working across both behavioral health and business operations.
- Implement and integrate AI tools. Lead the rollout of approved, compliance\-reviewed AI tools only (e.g., generative AI assistants, clinical documentation support, scheduling optimization, intake and admissions automation, billing/revenue\-cycle support, analytics dashboards) into existing systems and daily workflows, ensuring PHI is never entered into unapproved public AI tools.
- Build reusable, standardized assets. Develop and maintain prompt libraries, templates, SOPs, and repeatable workflows so that AI use across Compass is consistent, safe, and easy to adopt.
- Pilot and measure outcomes. Design and run focused pilot projects with clear, measurable goals, such as documentation time saved, reduced no\-shows, faster intake response, improved claim accuracy, and higher staff adoption; then scale what works.
- Train, coach, and enable staff. Create and deliver role\-specific training so clinical and business staff feel confident and capable using AI well. Act as an approachable internal expert and ongoing resource.
- Lead change management. Listen to frontline staff, translate workflow frustrations into practical AI solutions, communicate wins, and help the organization see AI as a tool that complements, never replaces, the human connection at the heart of care.
- Establish responsible AI governance. Collaborate with compliance, IT, and leadership to create and maintain AI usage guidelines, HIPAA and PHI safeguards, vendor evaluation standards, and review processes that keep Compass aligned with evolving regulatory and ethical requirements.
Required Qualifications
- Bachelor's degree in Information Technology, Computer Science, Data Science, Business, Healthcare Administration, or a related field, or equivalent demonstrated experience.
- Applied experience implementing or operationalizing AI, automation, or generative AI tools in a real business or healthcare setting.
- Strong working knowledge of generative AI tools (e.g., ChatGPT, Microsoft Copilot, Google Gemini, Claude) and workflow/automation platforms.
- Demonstrated ability to translate technical concepts for non\-technical stakeholders and to coach people who are new to AI.
- Experience designing and running pilots with measurable outcomes and reporting results to leadership.
- Solid understanding of data privacy, security, and compliance principles, including HIPAA as it applies to protected health information (PHI).
- Excellent communication, facilitation, and problem\-solving skills, with a collaborative, service\-oriented mindset
Important Note on Scope
At Compass Behavioral Group, AI is an adjunctive tool that supports clinicians and staff. It does not diagnose, treat, triage crises independently, or replace licensed clinical judgment. The AI Implementation \& Adoption Specialist will help the organization adopt AI responsibly within a robust ethical and regulatory framework that preserves the human connection central to behavioral health careWhat We Offer
Benefits:* Competitive Salary
- Paid time off
- Retirement plan
- Medical, dental, and vision insurance
- Short\-term and long\-term disability coverage
- Life insurance
- Health Savings Account (HSA)
- 401(k) with company match
- Mileage, phone, and supplies reimbursement
- Employee bonuses and advancement opportunities
- A supportive, team\-oriented work environment with a culture of continual learning and mutual accountability.
- Meaningful work that directly improves how we serve youth, adults, and families across Virginia.
- Ongoing professional development and the opportunity to shape AI strategy at one of Virginia's leading behavioral health providers.
Take the Next Step in Your Career!
Join Compass and discover a company that values your expertise, supports your growth, and empowers you to make a meaningful difference in the lives of the children and families we serve.
To learn more about Compass, watch this short video: https://compassva.com/discoverwhatsnext.
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Salary Context
This $52K-$72K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Compass Behavioral Group, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($62K) sits 71% below the category median. Disclosed range: $52K to $72K.
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.
Compass Behavioral Group AI Hiring
Compass Behavioral Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in VA, US. Compensation range: $72K - $72K.
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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