Interested in this AI/ML Engineer role at Gap Inc.?
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About This Role
About Gap Inc.
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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
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Gap Inc. is seeking a Senior Machine Learning Engineer with 10\+ years of experience to design, build, and scale production\-grade machine learning and AI systems that power data\-driven decision making across the enterprise.This role is focused on end\-to\-end ML system ownership, including data pipelines, feature engineering, model training, deployment, monitoring, and continuous optimization. You will lead the development of scalable ML platforms, drive best practices in MLOps, and enable reliable, high\-performance model inference in both batch and real\-time environments.The ideal candidate combines strong software engineering expertise with deep ML knowledge and has experience building robust, scalable ML systems in production, including modern applications involving large language models (LLMs) and agent\-based AI systems.
What you'll do
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- Architect and build scalable, production\-grade ML systems from experimentation to deployment and lifecycle management
- Design and implement end\-to\-end ML pipelines, including data ingestion, feature engineering, training, validation, and inference
- Develop and maintain high\-performance model serving systems using APIs (e.g., FastAPI) for real\-time and batch inference
- Lead the design and implementation of feature stores and reusable feature pipelines across teams
- Build and optimize distributed data processing workflows using Spark, Databricks, or similar platforms
- Implement and enforce MLOps best practices, including CI/CD pipelines, automated retraining, model versioning, and experiment tracking
- Design and manage model monitoring and observability frameworks to track performance, drift, latency, and system health
- Drive strategies for model retraining, drift detection, and continuous improvement
- Collaborate closely with data engineers, platform teams, and product stakeholders to integrate ML solutions into production systems
- Contribute to the adoption of modern AI capabilities, including LLMs, vector databases, retrieval\-augmented generation (RAG), and agentic workflows
- Ensure high standards of code quality, testing, documentation, and reproducibility
Who you are
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- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
- 10\+ years of experience in machine learning, software engineering, or related roles, with significant experience in production ML systems
- Strong programming expertise in Python and solid software engineering fundamentals (data structures, system design, APIs)
- Extensive experience with ML frameworks such as scikit\-learn, XGBoost, PyTorch, or TensorFlow
- Proven experience designing and deploying scalable ML pipelines and services in production
- Hands\-on experience with model serving frameworks and API development (e.g., FastAPI, Flask)
- Strong experience with containerization (Docker) and orchestration platforms such as Kubernetes
- Experience working with cloud platforms (GCP, AWS, or Azure) and building cloud\-native ML solutions
- Deep understanding of ML lifecycle management, including training, evaluation, deployment, monitoring, and retraining
- Experience implementing CI/CD pipelines for ML workflows and managing version control systems (Git)
- Strong experience with SQL and distributed data processing frameworks (e.g., Spark, PySpark)
- Excellent problem\-solving skills and ability to design scalable, maintainable systems
Salary Range: $181,400\.00 \- $235,800\.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.
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- 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 $181K-$235K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($208K) sits 15% above the category median. Disclosed range: $181K to $235K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Gap Inc. AI Hiring
Gap Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in SC, US. Compensation range: $235K - $235K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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