Interested in this AI/ML Engineer role at Hayes Company LLC?
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The Director of AI Strategy and Governance establishes the company's enterprise artificial intelligence strategy, governance framework, and center of excellence. This position sets the direction, standards, and guardrails for how AI is adopted across warehousing, fulfillment, logistics, and corporate functions, and ensures every initiative is explainable, secure, and tied to measurable business value. Reporting to the Vice President of Business Excellence, the Director chairs the enterprise AI Steering Committee and serves as the company's primary voice on responsible AI use. The Director partners closely with IT and Operations and Analytics Excellence (OAX), who own technical architecture and hands on automation delivery, so that this role stays focused on strategy, prioritization, governance, and value realization rather than day to day build work.Essential Duties and Responsibilities:* Develops and maintains the company's enterprise AI strategy and multiyear roadmap, aligned with growth, operational excellence, customer service, and risk goals.
- Establishes an enterprise process for submitting, evaluating, prioritizing, and approving AI opportunities, and develops investment recommendations and business cases for executive approval.
- Establishes and leads an enterprise AI Center of Excellence, setting operating standards, governance requirements, and performance expectations for teams building AI or automation solutions.
- Creates a centralized inventory of AI use cases, tools, models, vendors, and deployed solutions, and develops an AI champion network across departments and operating locations.
- Establishes and administers the company's responsible AI governance framework and chairs the enterprise AI Steering Committee.
- Develops policies governing acceptable AI use by employees, contractors, and vendors, and sets approval requirements based on the risk of each use case.
- Defines when human review, legal approval, security review, or executive authorization is required, and ensures AI recommendations are explainable and documented.
- Partners with Legal, HR, Finance, and IT to keep AI use compliant with applicable law, contracts, and company policy.
- Sets evaluation criteria for third party AI vendors and platform partnerships, and reviews proposed solutions against company standards for security, data use, and business value.
- Partners with OAX and Operations to prioritize where AI can most improve receiving, putaway, slotting, picking, packing, shipping, and inventory control, without owning day to day solution build.
- Partners with IT and OAX to define the standard for moving a successful pilot into a secure, supported production environment.
- Owns the enterprise portfolio of AI initiatives, prioritizing investment based on strategic value, financial impact, and risk.
- Develops the annual AI budget and a standardized method for calculating investment, savings, and return on investment, validated with Finance.
- Develops executive dashboards on portfolio status, investment, benefits, and risk, and recommends stopping or redesigning initiatives that are not delivering value.
- Recruits and develops a small strategy, governance, and program management team as the function matures.
- Partners with HR and Training on role based AI education, communication, and adoption strategy across the company.
Required Qualifications:* Bachelor's degree in business, supply chain, information technology, data analytics, or a related discipline.
- 8+ years of progressive experience in technology strategy, AI, analytics, or operational excellence, including experience shaping strategy for a cross functional program.
- 5+ years of leadership experience, including responsibility for enterprise programs or cross functional teams.
- Demonstrated success developing business cases and governance frameworks that produced measurable results.
- Strong executive presentation and communication experience, with comfort leading through influence rather than direct technical ownership.
- Strong financial analysis and business case development capability.
- Strong judgment regarding data privacy, responsible AI, and business risk.
Preferred Qualifications:* Master's degree in business, supply chain, analytics, or a related discipline.
- Experience establishing or leading an AI or digital transformation center of excellence or governance function.
- Experience in warehousing, fulfillment, transportation, or third party logistics.
- Knowledge of AI risk, privacy, and regulatory frameworks.
Physical Demands:* Ability to work in office and, periodically, warehouse and distribution center environments.
- Ability to walk operational areas to observe processes relevant to AI initiatives.
Work Environment:* Primarily office based, with periodic time in warehouse and distribution center environments.
- Mix of executive, cross functional, and committee meetings across corporate and operating locations.
Travel Requirements:* Travel to company facilities and vendor sites, approximately 15–20% of the time, depending on program activity. Equipment Used:* Standard office and presentation equipment (computer, projector, phone).
- Business intelligence, portfolio management, and collaboration platforms.
Supervisory Responsibilities:* Builds and leads a strategy, governance, and program management team as the function grows. Why You'll Love Working with Us:* Health Coverage That Starts Fast – Medical, Dental & Vision kicks in the first of the month after just 30 days.
- Smile on Us – We're covering your dental premiums for all of 2026!
- Security You Can Count On – Paid Life Insurance + 401(k) with company match.
- Time Off from Day One – PTO, holidays, and 2 floating holidays for flexibility.
- Weekly Paychecks – Because waiting is overrated.
- Invest in YOU – Full tuition grants & tuition reimbursement.
- Celebrate Success – Quarterly performance bonuses. Hayes Company/Knight Swift Supply Chain is an equal opportunity employer and considers qualified applicants for employment regardless of race, gender identity, gender expression, age, color, religion, disability, veteran's status, sexual orientation, or any other protected factor.
Salary Context
This $145K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Hayes Company LLC, 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 in Demand for This Role
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 $212,040 based on 13,780 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($157K) sits 26% below the category median. Disclosed range: $145K to $170K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Hayes Company LLC AI Hiring
Hayes Company LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US. Compensation range: $170K - $170K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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