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About This Role
### Description
We are seeking an expert VP of F&I Training to serve as a primary catalyst for dealership performance. This role is dedicated to protecting and elevating organizational standards by training dealership personnel on Finance & Insurance (F&I) products, sales processes, and operational workflows. You will be responsible for ensuring that F&I materials and systems are optimized, scalable, and delivered with excellence across our partner network.
This position is ideal for a high-accountability professional who thrives on precision and possesses the "Situational Leadership" skills necessary to coach dealership teams to peak profitability.
### Key Responsibilities
- F&I Performance Training: Deliver expert-level training programs through regional roadshows and our dedicated in-office Academy to align dealership teams with best practices.
- Product & Menu Mastery: Conduct comprehensive menu training to enhance sales effectiveness, ensuring F&I Managers understand how to maximize the value of all available resources.
- Operational Optimization: Evaluate and standardize F&I workflows to ensure they are repeatable, compliant, and aligned with industry-leading standards.
- Strategic Partnership: Collaborate with the Sales VP team to acquire new accounts by presenting impact studies that demonstrate measurable improvements in dealership outcomes.
- Field Support & Engagement: Work closely with field representatives to identify specific training gaps and provide tailored content for dealership personnel.
- Digital Learning Integration: Promote and support the Online Academy to ensure dealership staff have continuous access to professional development.
- Compliance & Quality Control: Monitor performance trends and implement corrective actions to ensure all dealership interactions meet high standards for professionalism and accuracy.
### Skills, Knowledge and Expertise
- Automotive Expertise: At least 5 years of experience in automotive operations, specifically within F&I.
- Leadership Background: Minimum of 3 years in a management role with a proven ability to influence and execute training strategies.
- Educational Excellence: Exceptional presentation and interpersonal skills, with a true passion for teaching and motivating others.
- Systems Proficiency: Comfortable working within CRM systems (e.g., Salesforce), menu providers, and HRIS platforms.
- Technical Knowledge: Strong working knowledge of dealership internal processes and F&I system access/navigation.
- Mobility: Ability to travel up to 75% of the time to support on-site dealership training events and roadshows.
- Professional Standards: A service-oriented mindset that prioritizes consistency, integrity, and accountability.
### About The Misch Group
Stone Hendricks Group is a direct-hire search firm that brings together years of experience and a diverse range of talent to connect businesses with exceptional job candidates. With a focus on timely and effective recruitment, we understand the power of a well-formed employee base in helping businesses achieve their goals. We offer our services to businesses of all sizes, providing qualified candidates for blue- and grey-collar roles, as well as white-collar and executive positions. The success of our direct-hire search process is driven by our advanced training, proprietary technology, and extensive network across industries. At Stone Hendricks Group, we value integrity and prioritize connectedness, commitment, and candor in our interactions with both employers and job seekers. Our clients consider us trusted advisors, relying on the highly personalized service we provide and our ability to find candidates that are an ideal fit for their unique needs. Choose Stone Hendricks Group for unsurpassed direct-hire search services that match successful organizations with talented job candidates.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At The Misch 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 $210,000 based on 1,345 positions with disclosed compensation.
Across all AI roles, the market median is $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. For comparison, the highest-paying categories include Research Scientist ($260,000) and AI Architect ($251,680). By seniority level: Entry: $125,000; Mid: $202,000; Senior: $240,000; Director: $255,600; VP: $225,000.
The Misch Group AI Hiring
The Misch Group has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $150K - $150K.
Remote Work Context
Remote AI roles pay a median of $193,725 across 129 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. Highest-paying categories: Research Scientist ($260,000 median, 48 roles); AI Architect ($251,680 median, 9 roles); Research Engineer ($250,200 median, 8 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $220,000. Top-quartile roles start at $260,000, and the 90th percentile reaches $311,800. 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. Research Scientist roles lead at $260,000 median, while AI/ML Engineer roles sit at $210,000. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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.
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