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About This Role
About Gap Inc.
At Gap Inc., we create culture as much as we create clothes. Our ambition is to become a high-performing house of iconic American brands that shape culture.
Our portfolio—Old Navy, Gap, Banana Republic, and Athleta—each brings a distinct point of view to how we show up in the world and serve our customers.
Old Navy democratizes style with quality and value for all. Gap champions originality through essential pieces that celebrate individuality. Banana Republic is rooted in a spirit of discovery, creating modern pieces inspired by craftsmanship and travel. Athleta champions the Power of She through confidence, strength, and movement.
We’re driven by a shared purpose: to bridge gaps—between people, perspectives, and possibilities—to create a better world.
We’re building a team that performs at a high level—people who think boldly, take ownership, and turn ideas into impact. If you’re ready to learn fast and help shape what’s next, you’ll fit right in.
About the role
In this role, you are the bridge between Data Science and the business — responsible for driving adoption of AI and ML models across Gap Inc. brands. You will operate as an internal consultant embedded within the Data Science organization: building trusted relationships with business stakeholders, translating complex model outputs into clear commercial narratives, and ensuring that every model the DS team builds actually drives measurable business impact. Success in this role looks less like coding and more like consulting — structured thinking, reliable delivery, and the ability to make technical complexity feel simple and actionable to merchant, planning, and sourcing leaders.
What you'll do
Model Adoption & Value Realization
- Own the end-to-end adoption lifecycle for a portfolio of DS models — from first stakeholder introduction through sustained, broad-based use
- Own and maintain a library of Model Explainability Cards — one-page business-language explainers for every production AI/ML model in the DS portfolio
- Design and coordinate adoption-focused A/B tests embedded in production business workflows, translating experiment results into business-language impact summaries
- Build and maintain adoption dashboards that track model coverage, influence rate, override rate, and time-to-adoption by function and brand
- Produce quarterly per-model business impact reports that quantify margin lift, forecast accuracy improvement, cycle-time reduction, and sell-through impact
- Diagnose adoption stalls by analyzing override analytics — identifying where and why humans deviate from model recommendations and routing structured findings back to model owners
Stakeholder Management & Internal Consulting
- Build and maintain trusted relationships with business partners across Merchandising, Inventory Planning, Sourcing, Design, and Costing — acting as their primary point of contact for all things related to DS model adoption
- Conduct structured discovery with business teams to diagnose adoption barriers, surface unmet needs, and develop tailored enablement plans by function and brand
- Develop and deliver executive-ready presentations, model explainability briefs, and quarterly business impact reports for senior stakeholders up to VP and SVP level
- Facilitate workshops, working sessions, and office hours that bring data science outputs to life for non-technical audiences
- Proactively manage a portfolio of business relationships — tracking open issues, commitments, and follow-through with a high standard of reliability and responsiveness
- Serve as the voice of the business back into the DS team, synthesizing stakeholder feedback and routing prioritized signal to model owners and engineers
Workflow Integration & Change Management
- Partner with business users and DS domain leads to redesign human-AI workflows so that model recommendations are embedded naturally in existing tools — PLM, planning platforms, costing tools, and sourcing systems — rather than requiring users to change behavior
- Train and coach business stakeholders on AI model interpretation, appropriate use, and feedback mechanisms; build repeatable onboarding materials that scale across brands
- Contribute to the institutional DS Enablement playbook, documenting what works, what doesn't, and how to accelerate adoption for future model launches
Who you are
- 3–6 years of experience in management consulting, customer success, or a client-facing analytics role; experience in a high-accountability, client-facing or internal consulting function is strongly preferred
- Demonstrated ability to manage multiple senior stakeholder relationships simultaneously with a high standard of responsiveness, follow-through, and structured communication
- Exceptional written and verbal communication skills — able to write crisp executive briefs, structure a compelling slide, and present confidently to VP-level audiences without relying on jargon
- Comfort operating in ambiguity: able to take an open-ended business problem, frame it clearly, and drive it to a concrete recommendation or deliverable without constant direction
- Sufficient data literacy to work credibly alongside a Data Science team — comfortable with concepts such as model accuracy, confidence intervals, feature importance, A/B testing, and business KPIs; does not need to build models but must be able to interrogate and interpret them
- Proficiency in SQL and/or Python for pulling data, building adoption metrics, and supporting light analytics; experience with Tableau, Looker, Power BI, or equivalent for dashboard development
- Experience designing and running structured experiments or pilots, with the ability to interpret results and translate statistical findings into plain-language business impact
- Familiarity with retail business processes — particularly Merchandising, Inventory Planning, Allocation, or Sourcing — is a meaningful advantage; multi-brand or omnichannel experience is a plus
- High-agency work style: proactively identifies blockers, manages up clearly, and brings a proposed solution alongside every problem
- Familiarity with MLOps concepts (model cards, drift monitoring, override analytics) is a plus; experience partnering with Data Science or Engineering teams in a previous role is an advantage
Salary Range: $117,700.00 - $153,000.00
Employee pay will vary based on factors such as qualifications, experience, skill level, competencies and work location. We will meet minimum wage or minimum of the pay range (whichever is higher) based on city, county and state requirements.
Benefits at Gap Inc.
- Merchandise discount for our brands: 50% off regular-priced merchandise at Old Navy, Gap, Banana Republic and Athleta, and 30% off at Outlet for all employees.
- One of the most competitive Paid Time Off plans in the industry.*
- Employees can take up to five “on the clock” hours each month to volunteer at a charity of their choice.*
- Extensive 401(k) plan with company matching for contributions up to four percent of an employee’s base pay.*
- Employee stock purchase plan.*
- Medical, dental, vision and life insurance.*
- See more of the benefits we offer.
- For eligible employees
Gap Inc. is an equal-opportunity employer and is committed to providing a workplace free from harassment and discrimination. We are committed to recruiting, hiring, training and promoting qualified people of all backgrounds, and make all employment decisions without regard to any protected status. We have received numerous awards for our long-held commitment to equality and will continue to foster a diverse and inclusive environment of belonging. In 2022, we were recognized by Forbes as one of the World's Best Employers and one of the Best Employers for Diversity.
Salary Context
This $117K-$153K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Gap Inc., 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($135K) sits 36% below the category median. Disclosed range: $117K to $153K.
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.
Gap Inc. AI Hiring
Gap Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Pleasanton, CA, San Francisco, CA, US. Compensation range: $153K - $153K.
Location Context
AI roles in San Francisco pay a median of $264,500 across 2,413 tracked positions. That's 24% 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 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
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