Interested in this AI/ML Engineer role at HEB?
Apply Now →Skills & Technologies
About This Role
Responsibilities:
H-E-B’s Corporate Planning and Analysis Team develops and maintains budgets and financial systems while providing current, reliable financial data, analysis, and technical information. The AI Center of Excellence (AI COE) is a new team inside P&A standing up the P&A AI Portal — a governed, self-serve way for non-technical partners across H-E-B to ask business questions in plain English and get answers they can trust, grounded in certified data, with visibility into how the answer was produced. As a Decision Scientist, your archetype is a Business Decision Scientist. We’re looking for someone who can bridge data and business domain expertise — someone who can codify how the business actually makes decisions into reusable context, and help build the process and platform that let other analysts across H-E-B leverage and contribute to that context, so their own work moves faster. Once you’re eligible, you’ll become an Owner in the company, so we’re looking for commitment, hard work, and focus on quality and Customer service. “Partner-owned” means our most important resources — People — drive the innovation, growth, and success that make H-E-B The Greatest Omnichannel Retailing Company. Do you have a: HEART FOR PEOPLE… willingness to take a break from algorithmic thinking to translate and share your findings with a variety of business customers? HEAD FOR BUSINESS… skills to blend rigorous decision science with real business acumen? PASSION FOR RESULTS… drive to generate business-valued questions and evidence-based recommendations, not just models? We are looking for: - a decision scientist who can bridge data and business domain expertise, codify business context so it can be reused, and help build the process and platform that let other analysts across H-E-B leverage and contribute to that context to accelerate their own work
What is the work?
- Works directly with business partners to understand how a decision actually gets made today, and codifies that judgment into reusable, shareable context — not a one-off analysis.
- Helps design and build the process and platform that let other analysts find, reuse, and contribute to that codified context, rather than everyone starting from scratch.
- Reasons about what a data-driven or AI-generated answer implies for the business, not just whether it’s technically correct — and can defend that reasoning to a business partner.
- Builds the feedback loop that turns real usage — what worked, what didn’t, what was missing — into concrete improvements to the shared context and platform.
- Applies causal and comparative reasoning to distinguish a real business signal from noise, seasonality, or a data-quality artifact before it reaches a partner as a recommendation.
- Helps set the standards for what “good” looks like as more analysts contribute — so the platform gets more useful, not less trustworthy, as it scales.
What is your background?
- A related degree (statistics, economics, data science, operations research, or similar) or comparable formal training, certification, or work experience
- 3+ years of experience in a retail or retail-related decision science, analytics, or data science role
- Demonstrated ability to go from a business question to a shipped, defensible recommendation — not just a model or a dashboard
- Comfortable working with imperfect, incomplete, or newly-modeled data (serve-layer tables still in progress, sparsely-populated join keys, etc.) and saying clearly what is and isn’t known yet
Do you have what it takes to be a Decision Scientist on the AI COE?
- Technical knowledge in SQL and Python; comfortable enough with data pipelines and applied AI tools to work alongside the people building them, without needing to build them yourself
- Working knowledge of causal reasoning, experiment design, or applied statistics — enough to question whether a surfaced pattern is a real driver or a spurious correlation
- Ability to work across multiple business domains without needing to become a deep specialist in each one first
- Ability as a creative storyteller and translator between business questions and technical capabilities
- Ability to define what “correct” or “good” looks like for a business question before anyone builds toward it
- Comfortable operating in a fast-moving build with a small team and shifting priorities
Preferred (more technical background):* Familiarity with back-end AI/ML platforms (e.g. Vertex AI, Databricks, or similar) — enough to understand what’s possible and speak the same language as the engineers building on them
- Comfortable working in a command-line environment (CLIs) as part of day-to-day work
- Experience building and deploying an application or tool end to end, even a small one — not just producing an analysis or notebook
- Exposure to version control (e.g. Git) and collaborative development workflows
Can you…
- Work in a fast-paced retail environment with frequently shifting priorities
- Work extended hours; sit for long periods
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At HEB, 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
HEB AI Hiring
HEB has 2 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer. Positions span Austin, TX, US, San Antonio, TX, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.