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About This Role
Job Title: Sales Enablement Specialist, AI
Job Summary:This role transforms how our sales team works by embedding AI directly into the commercial workflow — reducing manual effort, improving decision quality, and accelerating revenue outcomes. The Sales Enablement Specialist, AI is the primary AI enabler for our commercial team: building prompt workflows, designing AI\-powered playbooks, and ensuring every rep leverages cutting\-edge tools to prospect smarter and close faster. This is a builder role. The ideal candidate brings genuine AI fluency — equally comfortable engineering a multi\-step prompt workflow as coaching a rep to use it. If you believe AI is the most powerful tool in sales right now and want to lead that charge, this role is for you.
Essential Functions:
Training \& Onboarding Support
- Ensure AI tool training is embedded in onboarding so every new hire is proficient with Claude, ChatGPT, and core AI workflows from day one.
- Design and deliver AI enablement workshops and certification programs that build rep confidence and competency across the sales cycle.
- Manage LMS (Absorb) to keep AI training content current, accessible, and tracked across commercial teams.
AI Workflow Development
- Build prompt\-based AI workflows for account planning, prospecting, and territory prioritization that measurably reduce rep workload.
- Maintain a living prompt library — categorized by sales stage, persona, and use case — with supporting guides and video walkthroughs to drive adoption.
- Evaluate new AI tools continuously; track adoption metrics and identify reps who need additional coaching.
Content, Playbooks \& Sales Process
- Partner with Product and Marketing to keep sales assets — data sheets, demos, battle cards, and launch kits — current and accessible.
- Define and maintain sales playbooks with AI\-enhanced components: prompt\-driven objection handling, call prep frameworks, and talk tracks by persona.
- Gather field and Sales Engineer feedback and rapidly translate it into updated training materials and workflow improvements.
Strategic Collaboration
- Bridge Sales Engineering, Product, Sales Ops, and Sales Leadership to align enablement content with field realities and technical demos.
- Communicate enablement and AI program results to executive leadership in a clear, data\-driven manner.
Minimum Requirements:
Specific Job Skills:* AI Proficiency: Hands\-on experience building multi\-step prompt workflows, reusable prompt templates across personas, and AI integrations into CRM and sales tools. Must show examples of work.
- Communication: Able to explain AI concepts to non\-technical audiences clearly and practically.
- Tooling Expertise: Experience with LMS platforms (Absorb preferred), CRM tools, AI note\-taking tools, and revenue intelligence platforms (Clari preferred).
- Organization: Strong project management skills; able to run multiple workflow builds and programs simultaneously.
- Builder mindset: Creates systems and workflows from scratch, not just manages existing ones.
- Curious and experimental: Actively tests new AI tools, iterates fast, and brings that energy to how reps work.
- Comfortable with ambiguity: Thrives where the playbook is still being written and rapid iteration is the norm.
- Systems thinker: Turns messy field feedback into clean, repeatable workflows and training materials.
- Sales\-empathetic: Designs tools that are practically useful in the field, not just technically elegant.
WHAT SUCCESS LOOKS LIKE IN 6–12 MONTHS
- Centralized AI prompt library deployed and actively used by \>70% of reps (organized by stage, persona, and use case)
- Account research and call prep time reduced by 30–50% through purpose\-built AI workflows
- AI workflows embedded into onboarding and core sales motions (account planning, prospecting, territory prioritization)
- Baseline AI usage metrics and reporting cadence established, giving leadership clear visibility into adoption
- % reduction in rep prep time • AI workflow adoption rate • New hire ramp time • Prompt library utilization
Education: Bachelor’s Degree
Experience: 3–6 years in Sales Enablement, Sales Operations, or a similar commercial role; healthcare technology or SaaS preferred.
Supervision: N/A
Certifications: N/A
Language Skills:
Ability to read, analyze and interpret general business periodicals, professional journals, technical procedures or governmental regulations. Ability to write reports, business correspondence and procedure manuals. Ability to effectively present information and respond to questions from a variety of both internal and external sources.
Physical Capabilities: Standard categories
The physical capabilities described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to sit; use hands to finger, handle, or feel; reach with hands and arms; and talk or hear. The employee is occasionally required to stand and walk. The employee must occasionally lift and/or move up to 10 pounds. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception, and ability to adjust focus.
RevSpring is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
Note: This Job Description may not describe all of the job responsibilities and standards assigned to this position. The duties may change from time to time. RevSpring does not discriminate against any group in hiring or employment practices. Nothing in this job description constitutes a contract for employment.
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 RevSpring, Inc, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
RevSpring, Inc AI Hiring
RevSpring, Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Phoenix, AZ, US, Boston, MA, US.
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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