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About This Role
About Sellers Dorsey
Sellers Dorsey is a healthcare impact strategy firm focused on improving care access, quality, and outcomes for our nation’s most vulnerable populations. We work with providers, managed care organizations, state entities, and others, to design, implement, fund, and optimize sustainable programs that deliver maximum impact to underserved communities. Built on decades of experience in Medicaid, our team includes former state Medicaid directors, healthcare policy experts, health plan execs, and hospital leaders who know how to navigate the complexities of the system and find creative, impactful solutions that drive the greatest impact for the individuals and communities that need it most.
About the Role
Sellers Dorsey is seeking a Senior AI Enablement Specialist who will drive adoption of the firm’s enterprise AI tools across Sellers Dorsey. This is an embedded, cross\-functional role. As the Senior AI Enablement Specialist, you will rotate through departments such as Finance \& Accounting, Legal, Consulting, Growth, and Operations, working directly alongside staff to apply Claude and similar AI capabilities to real deliverables: financial models, contract review, client materials, and analysis. The role sits within Product \& Technology and serves as a two\-way bridge, bringing P\&T’s tools and capabilities to the firm and channeling field\-level needs and product ideas back to Product Management for roadmap consideration. Success is measured by breadth and depth of adoption, tangible productivity gains, and staff confidence in using AI tools well and safely.
Key Responsibilities
1\- Embedded AI Enablement \& Adoption:
- Rotate through firm departments to work hands\-on with staff applying Claude and Microsoft 365 AI to their actual work.
- Develop a transparent, criteria\-based prioritization method for department rotations and/or use cases and translate it into an AI Enablement roadmap approved by leadership to work against.
- Diagnose where AI can remove effort or raise quality, then co\-build solutions in the flow of work: models, decks, documents, analysis, and contract\-review support.
- Leave each team measurably more capable and self\-sufficient than before the engagement.
2\- Training, Coaching \& Best Practices:
- Deliver hands\-on coaching, office hours, and just\-in\-time guidance tailored to each department’s work.
- Build and maintain reusable prompts, templates, playbooks, and reference materials.
- Move staff from basic to confident, high\-value, and safe use of AI tools.
3\- Roadmap \& Product Feedback Loop:
- Capture field\-level needs, friction, and product ideas surfaced while embedded.
- Translate them into structured intake for Product Management and help triage tech\-enabled opportunities.
- Serve as P\&T’s ear to the ground across the firm.
4\- Responsible \& Secure AI Use:
- Reinforce firm policy on data handling, PHI, confidentiality, and approved tools in every engagement.
- Model compliant AI practices (HIPAA, firm AI governance) and escalate risky patterns.
- Ensure adoption never outruns the firm’s security and compliance posture.
5\- Measurement \& Reporting:
- Track adoption, use cases, and productivity outcomes across departments.
- Report results and ROI to P\&T leadership and surface high\-leverage patterns worth scaling firm\-wide.
Key Qualifications
- Bachelor's Degree in Business, Information Systems, Communications, Healthcare Administration, Data/Analytics, or related field. Equivalent professional experience considered.
- 5\+ years combining business/operations, consulting, training/enablement, or solutions delivery, with demonstrated hands\-on use of generative AI tools in a professional setting.
- Proven ability to work independently across multiple functions and ramp quickly in unfamiliar domains.
- Track record of driving tool or process adoption and turning business problems into practical, usable solutions.
- Excellent communication and coaching skills with both technical and non\-technical audiences at every level.
- Hands\-on with Claude or comparable enterprise AI assistants.
- Healthcare, consulting, or other regulated environment experience preferred.
- Financial modeling in Excel and/or executive presentation development in PowerPoint experience preferred.
- Exposure to contract review, Medicaid / healthcare finance, or analytics/BI preferred.
- Prior AI champion, enablement, or center\-of\-excellence experience preferred.
Critical Professional related Technical Skills
- Advanced, hands\-on proficiency with Claude (Desktop, Claude for Excel, Claude for PowerPoint) and prompt design.
- Strong Microsoft 365 skills: Excel (modeling), PowerPoint, Word, Teams, SharePoint.
- Ability to build and improve real deliverables: models, analyses, presentations, and documents.
- Working familiarity with P\&T capabilities and the Constyn platform sufficient to intake and route product ideas.
- Learns new tools quickly and explains them simply.
Preferred Education and Certification
- Change management certification (e.g., Prosci / ADKAR).
- AI / prompt\-engineering credential or equivalent demonstrated proficiency.
- Instructional design or adult\-learning training.
- Microsoft 365 or analytics (e.g., Power BI) certifications.
Other Requirements
- Sound judgment with confidential and sensitive information; strict adherence to HIPAA, data\-handling, and firm AI governance policies. No PHI in unapproved tools.
- Builds trust and influences across all levels and functions without direct authority.
- Self\-directed and highly organized; manages a rotating, multi\-department schedule.
- Simple approaches, critical thinking, curiosity, initiative. Comfortable navigating ambiguity.
Compensation \& Benefits
The anticipated salary range for candidates is $105,400/year in our lowest geographic market range to up to $145,000/year in our highest geographic market range. The final pay offered to a successful candidate will be dependent on several factors that may include but are not limited to the type and years of experience within the job, the type of years and experience within the industry, the candidate’s education, and the candidate’s market location. Typically, candidates are not hired near the top of the range and compensation decisions are made based upon Sellers Dorsey’s Total Compensation Policies \& Guidelines. The successful candidate will also be eligible to participate in our annual Corporate Incentive Plan (CIP) that can range to up to 10% of annual salary.
Provided they meet all eligibility requirements under the applicable plan documents, the successful candidate (and their eligible dependents) will be eligible to enroll in group healthcare plans that offer medical, dental, and vision and for insurance plans offering short term disability, long term disability, and basic life. Employees are also able to enroll in Sellers Dorsey’s 401k plan provided they meet plan requirements. Sellers Dorsey offers a Flexible Time Off that allows employees to use what they need. Additionally, we offer 16 paid holidays throughout the calendar year, paid time off for qualifying medical leave, and up to 12 weeks of combined paid parental and bonding leave. The foregoing benefits and paid time off, including an employee’s eligibility therefore, will be controlled by applicable plan documents and Sellers Dorsey policy.
This is intended to provide a general description of benefits and other compensation and is not a substitute for applicable plan documents or company policies.
Sellers Dorsey is an Equal Employment/Affirmative Action employer. We do not discriminate in hiring on the basis of sex, gender identity, sexual orientation, race, color, religious creed, national origin, physical or mental disability, protected Veteran status, or any other characteristic protected by federal, state, or local law.
If you need a reasonable accommodation for any part of the employment process, please contact us by email at [email protected] and let us know the nature of your request and your contact information. Requests for accommodation will be considered on a case\-by\-case basis. Please note that only inquiries concerning a request for reasonable accommodation will be responded to from this e\-mail address.
Sellers Dorsey maintains a Drug\-Free workplace.
Salary Context
This $105K-$145K 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 Sellers Dorsey, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($125K) sits 42% below the category median. Disclosed range: $105K to $145K.
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.
Sellers Dorsey AI Hiring
Sellers Dorsey has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $145K - $145K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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