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About This Role
About Us
The Freeman Company is a global leader in events, on a mission to redefine live for a new era. With a data-driven approach and the industry’s largest network of experts, The Freeman Company's insights shape exhibitions, exhibits, and events that drive audiences to action. The integrated full-service solutions leverage a 99-year legacy in event management as well as new technologies to deliver moments that matter.
Summary
Freeman's Growth organization is executing an enterprise AI mandate — moving from AI exploration to AI leadership across Strategic Sales Planning (SSP) and the broader Growth org. This role leads the AI Strategy & Automation function, which serves as the operational engine of that mandate: building and deploying the AI use cases that make up our AI strategy, and running the governance and adoption-measurement work that keeps that deployment disciplined. It reports directly to the SVP of Strategic Sales Planning, who owns the mandate's strategic direction and executive relationships.
The Manager is accountable for the results of the AI Strategy & Automation function: setting priorities, adapting SSP's departmental plans to resource and operational challenges, and providing technical guidance on the work. This is a hybrid leadership-and-builder role, not a pure strategy seat and not a pure technical build seat. The right person can build and deploy AI use cases today, moves fluidly between hands-on problem solving and the framework work that keeps AI deployment safe, measured, and adopted, and is ready to grow into leading a team.
This position will support our Strategic Sales Planning group under our Growth (Sales) team. It is eligible to work a hybrid schedule, generally requiring work in-office and/or show site 2-3 days per week. The ideal candidate will be based out of any of the following locations:
- Dallas, TX
- Orlando, FL
- Alexandria, VA
- Chicago, IL
- Las Vegas, NV
- Nashville, TN
- Atlanta, GA
Essential Duties & Responsibilities
Leadership & Team Building
- Build the AI Strategy & Automation team over time: define the operating model, roles, and hiring plan as the use case portfolio scales, and partner with the SVP and HR/Talent Acquisition to recruit and onboard.
- Own the function's results: set priorities, allocate effort across use cases, and ensure quality and timely delivery, leading through partnership and influence with SSP's analytics team until a dedicated team is in place.
- Adapt SSP's departmental plans to resource and operational challenges, and escalate trade-offs and capacity constraints to the SVP with clear recommendations, including when and where to add headcount.
- Provide technical guidance to colleagues and business partners on Salesforce, Power BI, Account Segmentor, and AI tooling (Claude, Microsoft Copilot).
Use Case Development & Deployment
- Lead the build and deployment of Strategic Sales Planning Fiscal Year 2027 priority AI use cases, personally at first and through the team as it grows: pipeline reporting automation, CRM hygiene monitoring, SLT/Ops report generation, Win/Loss intelligence, and Segmentor insight narration.
- Stay hands-on where it matters: unblock complex technical and operational problems and move each use case from concept to a working, adopted integration.
AI Governance & Adoption
- Own the Growth org's AI governance framework and, as the team grows, direct its upkeep: usage guardrails, data handling standards, and a taxonomy of what AI does vs. what people own.
- Lead adoption tracking and reporting, measuring actual workflow integration and time savings rather than install rates.
- Partner with Training & Development on enablement and change management as use cases roll out.
Cross-Org Partnership
- Work day-to-day with the Sr. Manager, Strategic Sales Planning & Marketing Liaison and the Sr. Sales Analyst on the analytics work underlying most FY2027 use cases.
- Partner with the Growth org's other AI owners (E&ES Strategic Business Development, CSS Guardians, Customer Experience, and mdg) and their teams as the portfolio expands beyond SSP, influencing them to adopt shared standards and practices.
- Coordinate with Enterprise Architecture & Engineering (IT) on tooling and infrastructure, and with the Salesforce Einstein team on AI tool integration (Einstein NBA, Account Segmentor, Account Plans).
Education & Experience
- 7+ years of progressive experience in analytics, sales operations, or business technology, including 2+ years leading people, teams, or workstreams.
- Experience coaching, prioritizing, and managing performance, and readiness to build a team from the ground up.
- Hands-on experience with some of: Salesforce, Power BI, Microsoft Copilot, Claude or a comparable AI assistant.
- Generalist who learns new tools quickly across Salesforce/Einstein, Power BI, and AI assistants.
- Solves technical and operational problems with an eye to department-wide impact.
- Translates ambiguous business problems into structured, automatable workflows.
- Understands how AI and analytics integrate with sales, marketing, and IT.
- Strong written communication for reporting to sales leadership Influences others, internally and externally, to adopt new approaches.
- Background in sales operations, revenue operations, or business intelligence.
- Exposure to CMMI or another process-maturity framework.
- Experience with workflow automation tools (e.g., Power Automate).
Travel Requirements
Travel up to 25%What We Offer
The Freeman Company provides benefits that aim to empower our people and their families to thrive mentally, physically, and financially. These are a handful of the types of programs and benefits our full-time people may be eligible for. There may be some variances in specific benefits across regions.
- Medical, Dental, Vision Insurance
- Tuition Reimbursement
- Paid Parental Leave
- Life, Accident and Disability
- Retirement with Company Match
- Paid Time Off
- Salary range of $125,000-$155,000 based upon experience
- Eligible for an annual bonus of up to 10% of the base salary
Diversity Commitment
At The Freeman Company, our commitment to diversity and inclusion is helping us to create not only a great place to work, but also an environment where our employees, our customers and our communities around the world can reach their goals and connect with each other. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status and other characteristic protected by federal, state or local laws.
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#TFCBrand
Salary Context
This $125K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Freeman, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $125K to $155K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Freeman AI Hiring
Freeman has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US. Compensation range: $155K - $155K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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