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About This Role
Burn Boot Camp Culture
Burn Boot Camp is one of the fastest\-growing fitness franchises in the nation. We move like our members — with purpose and at full speed. Our mission is to inspire, empower, and transform lives through community\-based fitness.Position Overview
Burn Boot Camp is one of the fastest\-growing fitness franchises in the country — 400\+ Gyms, a fiercely loyal Member base, and an aggressive growth path to 1,000 locations by 2028\. Franchise Partners operate independent businesses within our national brand. What they have never had — until now — is enterprise\-grade intelligence delivered daily: a clear signal on what to prioritize, what is at risk, and what is working. Building that intelligence layer is the mandate behind this role.
We are looking for a Head of AI to own that layer end\-to\-end. This is not a research role. It is a builder's role — someone who can architect AI systems that reach every Franchise Partner daily, lead a modern data platform, and translate strategy into shipped product. You will sit inside the Technology organization, report to the VP of Technology, and operate at the intersection of data engineering, applied AI, and franchise operations. *We are making foundational platform decisions right now. The person we hire will shape the architecture, own the roadmap, and build the team that carries it forward.* ResponsibilitiesAI Strategy \& Product Roadmap* Define and drive Burn's enterprise AI strategy — from Franchise Partner intelligence to operational automation to generative AI applications.
- Own the AI product roadmap across multiple active workstreams, sequencing priorities against business impact and technical readiness.
- Lead architecture decisions: data pipelines, model serving, LLM integration, API design, and the feedback loops that make AI outputs better over time.
- Partner directly with operations, marketing, franchise development, and finance to identify where AI creates the most leverage.
Franchise Partner Intelligence* Architect and deliver AI\-powered tools that surface actionable daily intelligence for 300–1,000 Franchise Partners — identifying what to prioritize, what is at risk, and what is driving performance.
- Own the end\-to\-end product experience: data ingestion, scoring models, AI\-generated recommendations, and the quality and confidence layer that ensures reliability at scale.
- Develop performance frameworks that translate raw data into decision\-ready insights — reducing dependence on manual analysis and field support escalations.
- Define and track Franchise Partner adoption as a first\-class success metric alongside accuracy and uptime.
AI Workstreams \& Applied Products
You will lead multiple simultaneous AI initiatives across the business. Active workstreams include:* Franchise Partner daily intelligence and performance analytics platform
- Generative AI for internal and Member\-facing applications
- AI\-powered lead nurture and CRM enrichment
- Intelligent automation for finance, operations, and service workflows
- Conversational AI and agent\-based tooling for internal teams
Data Platform Leadership
The AI strategy runs on the data platform — and you'll own both. You'll own the strategy and lead the team that executes it, including architecture, governance, and roadmap for:* Snowflake — our enterprise data warehouse. You'll own the platform strategy, data modeling standards, cost governance, virtual warehouse design, and the roadmap for expanding Snowflake as our single source of truth across all data sources.
- Operational Data Store (ODS) — the integration and transformation layer connecting source systems (membership, POS, CRM, franchise ops) to analytics and AI. You'll define the architecture that keeps data clean, current, and trustworthy.
- Domo — Burn's primary BI platform for home office reporting, franchise development dashboards, and executive analytics. You'll own the roadmap for what Domo solves, what Snowflake serves directly, and how the two layers complement each other.
- End\-to\-end data governance: field\-level definitions, data dictionaries, freshness SLAs, quality monitoring, and the access controls that protect 300\+ Gyms' business data.
Team \& Vendor Leadership* Build and lead the Data \& AI team — hiring, developing, and retaining engineers and analysts who build in production. You'll inherit a team and have the mandate and budget to grow it as the roadmap scales.
- Manage key vendor and implementation partner relationships, including the technology partner responsible for platform delivery.
- Set engineering standards: code quality, testing, documentation, security, and deployment practices across all AI and data systems.
- Represent AI and data at the Technology Advisory Committee and in cross\-functional leadership forums.
QualificationsRequired* 7\+ years in data engineering, ML engineering, or AI product roles — with a track record of shipping AI systems in production at scale.
- Hands\-on experience with cloud data warehouse platforms — Snowflake strongly preferred.
- Deep fluency in modern data stack: ELT/ETL design, data modeling, API integration patterns, and real\-time vs. batch pipeline trade\-offs.
- Experience designing and deploying LLM\-based or generative AI products — prompt engineering, retrieval\-augmented generation, output quality and safety.
- Strong product instincts: ability to translate complex, ambiguous business problems into well\-scoped AI solutions with clear success criteria.
- Demonstrated ability to lead cross\-functional initiatives and manage vendor/partner relationships.
Strongly Preferred* Background in franchise, multi\-location retail, fitness, or hospitality — with working knowledge of unit\-level economics and how operators make day\-to\-day business decisions.
- Experience designing data products for operationally focused, non\-technical end users — where simplicity, trust, and adoption are as important as analytical depth.
- Familiarity with Domo, HubSpot, Loopspark, or similar franchise, CRM, and membership technology ecosystems.
- Track record of building high\-performing teams and developing talent, not solely inheriting established organizations.
*This position description is not intended to be and should not be construed as an all\-inclusive list of responsibilities, skills or working conditions associated with this position. While this description is intended to accurately reflect the position's activities and requirements, management reserves the right to modify, add or remove duties as necessary.*
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 Burn Boot Camp, 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.
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.
Burn Boot Camp AI Hiring
Burn Boot Camp has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cornelius, NC, 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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