Interested in this AI/ML Engineer role at Foot Locker?
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About This Role
Overview:
Hybrid (3 days in NYC office) At this time, Foot Locker will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F\-1 OPT, F\-1 STEM OPT, F\-1 CPT, J\-1, TN, or another type of work authorization).
Foot Locker Inc. is seeking a highly motivated Senior AI \& Analytics Analyst to drive high\-impact insights and build AI\-powered, self\-service decision capabilities across Foot Locker. In this role, you will operate at the intersection of business, data, and AI—transforming complex data into actionable insights and scalable solutions that enable smarter, faster decision\-making. You’ll partner closely with cross\-functional teams to design and deliver analytics products that enhance retail intelligence, operations, and customer experience.
This position goes beyond traditional analytics, focusing on embedding intelligence into workflows through automation, semantic modeling, and AI\-enabled tools. You will play a key role in shifting the organization from reactive reporting to proactive, data\-driven strategy. If you are passionate about solving complex problems and applying modern AI approaches in a dynamic retail environment, this role offers the opportunity to make a meaningful enterprise\-wide impact.
Great brands reflect culture. The best ones help shape it. At Foot Locker, we’re rooted in sport, powered by style, and driven by the communities that move both. From Foot Locker and Kids Foot Locker to Champs Sports, WSS, and atmos, our brands sit at the intersection of sport, style, and self\-expression.
As part of the DICK’S Sporting Goods family, you’re backed by the resources, reach, and career opportunities of a world\-class sports company. That unleashes Foot Locker to keep pushing forward with the agility and edge that define the brand. We’re looking for great people who want to influence culture and the generation that is shaping what’s next.
Whether you’re building strategy, shaping innovations, or creating unforgettable customer moments, you’ll contribute to decisions and experiences that define our brands’ impact in the communities we serve.
Come change the game with us. Apply today!
Responsibilities:
Deliver Insights \& Drive Decisions
- Perform deep\-dive analysis across key business areas, with emphasis on Retail Intelligence
- Translate business questions into technical requirements, structured analysis and actionable insights
- Define and monitor KPIs aligned to business goals
- Support leadership with recurring and ad hoc decision analysis
- Identify opportunities to scale insights through automation and self\-service
Build AI\-Powered Analytics Capabilities
- Design and develop self\-service analytics tools powered by AI
- Create scalable, analytics\-ready datasets and semantic layers
- Apply context engineering (data, logic, business rules) to ensure accuracy and insightful responses.
- Enable natural language querying, enhanced self\-service analytics, automated insight generation, and decision intelligence support.
- Continuously improving tools based on user behavior and business needs
Support Advanced AI Solutions
- Support engineers to develop and deploy AI\-driven solutions
- Define use cases, requirements, and success metrics
- Contribute to solution design (data structure, context, validation)
- Work with modern AI approaches (e.g., RAG, prompt engineering, agent workflows)
- Participate in prototyping, testing, and iterative development
Cross\-Collaborate Across the Business
- Translating business needs into data and AI solutions
- Partner with analytics, engineering, and business teams
- Ensure consistency in metrics and definitions
- Communicating insights clearly to technical and non\-technical audiences
Work within Agile environments
Qualifications:
- 4\+ years in analytics, data, or a related field
- Proven ability to deliver insights and drive business impact
- Experience working across both business and technical teams
- SQL (advanced): large\-scale data transformation and optimization
- Analytics: strong problem\-solving, statistical thinking, and insight generation
- Data Modeling: building scalable datasets and semantic layers
- Working Knowledge of AI Fundamentals: familiarity with
- RAG, embeddings, prompt engineering
- Agent frameworks (e.g., LangChain, Semantic Kernel)
- Model evaluation and design trade\-offs
Nice to Have Skills (Not Required)
- Retail industry experience (merchandising, planning, operations)
- Experience with AI\-enabled BI (e.g., Power BI, Databricks, Snowflake)
- Python for analytics or prototyping
- Exposure to vector databases, semantic search, or knowledge systems
- Agile experience (e.g., JIRA)
At Foot Locker, we value innovation, authenticity, and integrity in all that we do. To uphold the security and fairness of our hiring process, we ask that candidates refrain from using AI tools, including ChatGPT, during interviews and assessments. To ensure a smooth and secure experience, please review the following guidelines:* Cameras must be on for all virtual interviews.
- AI tools are strictly prohibited during interviews or assessments.
We appreciate your understanding and cooperation as we work together to create a transparent and equitable hiring experience.
Benefits:
The annual base salary range is $95,000 \- $115,000/ year. This range represents the anticipated low and high end of the salary for this position. This role is also eligible to receive short term incentives that align with individual and company performance. Salary will be determined by the education, experience, knowledge, skills, and abilities of the applicant, internal equity, and alignment with market data. Salary is one component of the Foot Locker, Inc. total compensation package, which includes the below.
Foot Locker Benefits:* Employee Discount
- Paid Time Off
- Medical \| Dental \| Vision Coverage
- 401(k) \| Roth 401(k)
- Life Insurance
- Flexible Spending Account
- Opportunities for Advancement
- Tuition Reimbursement for Qualified Courses
- Strong Company Culture
- Employee Resource Groups
\#LI\-KS1
Salary Context
This $95K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Foot Locker, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($105K) sits 41% below the category median. Disclosed range: $95K to $115K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Foot Locker AI Hiring
Foot Locker has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $115K - $115K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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