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About This Role
Who We Are
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At Academy Sports \+ Outdoors our vision is to be the best sports \+ outdoors retailer in the country — but what truly sets us apart is our people. We’re a passionate, purpose\-driven team that’s as committed to each other as we are to our customers.
We’ve spent over 80 years building a culture that puts people first. We believe in creating opportunities for growth, fostering meaningful connections, and supporting every Team Member’s journey. What fuels us? Our belief in the power of fun.
Here, you won’t just help customers gear up for their next adventure — you’ll launch one of your own. Whether you're starting out or leveling up, Academy is a place where fun can’t lose!
Education:
- Bachelor’s in engineering, Statistics, Data Science, or Computer Sciences
- Master’s degree in Analytics or data science (preferred)
Work Experiences:
- 8\+ years of experience in data science, advanced analytics, applied machine learning, or related fields. Experience in retail or B2C is preferred
- 4\+ years of experience leading teams or major technical initiatives, including people management or matrixed leadership
- 3\+ years of experience in B2C, retail, e\-commerce, marketing analytics, or customer\-facing analytics domains
- 3\+ years of experience building, deploying, or enabling production\-grade analytics or ML solutions
Skills:
- Strong engineering mindset with experience in: Modular code, Reproducibility \& Production\-grade analytics or ML systems
- Experience working with digital analytics, customer data platforms, or experimentation data (e.g., Adobe, web/app analytics, or similar ecosystems)
- Proven ability to influence across matrixed organizations without direct ownership
- Experience partnering with data engineering, platform, and governance teams
- Ability to balance speed to value with long\-term scalability
- Experience enabling MLOps or analytics platforms, preferred
- Exposure to customer analytics, personalization, marketing, or e\-commerce use cases, preferred
- Experience operating in early\-to\-mid maturity data organizations, preferred
- Comfort shaping standards without formal authority, preferred
Responsibilities:
Platform \& AI Standards Enablement
- Define and evolve reusable data, feature, and modeling patterns, MLOps and model lifecycle standards, and production\-ready analytics/ML solutions.
- Partner with CIO Data Engineering and Platform teams to influence canonical customer data models, shared datasets, feature reuse, and data access/consumption standards.
Customer Domain Translation \& Enablement
- Translate customer, marketing, and omnichannel needs into scalable technical and platform\-aligned patterns.
- Enable domain\-aligned data scientists, analysts, and engineers to adopt standards, reduce reinvention, and accelerate delivery.
- + Influence enterprise priorities through evidence, design proposals, and proof\-of\-value work.
Governance, Quality \& Responsible AI
- Represent customer\-domain data needs in governance forums.
- Help evolve governance standards that are practical for personalization, experimentation, and customer analytics.
- Ensure quality, fairness, trust, and compliance are embedded by design.
Customer Data Domain Enablement
- Lead Adobe Analytics and Quantum Metrics data capture, acting as a catalyst for effective use across analytics, personalization, and experimentation.
- Serve as the data domain owner for all customer\-facing data domains, including tag management, Bridg, and clean room capabilities, accountable for data definitions, quality, access, and downstream usability.
- Partner with IT pods, platform, and engineering teams to define and govern customer data flows securely and at scale through well\-documented integrations.
Leadership Attributes
- Systems thinker with a pragmatic bias toward delivery.
- Trusted by both business and technical stakeholders.
- Comfortable letting teams execute independently while influencing across a matrixed organization.
- Optimizes for scale and reuse, not personal ownership.
Physical Requirements \& Attendance:
- Acceptable level of hearing and vision to perform job duties
- Adhere to company work hours, policies, procedures, and rules governing professional staff behavior
- Regular attendance in the office is required
Equal Employment Opportunity
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Academy is an Equal Opportunity Employer and does not discriminate with regard to employment opportunities or practices on the basis of race, religion, national origin, sex, age, disability, gender identity, sexual orientation, or any other category protected by law.
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 Academy Sports + Outdoors, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Academy Sports + Outdoors AI Hiring
Academy Sports + Outdoors has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Katy, TX, 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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