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### About Stitch Fix, Inc.
Stitch Fix (NASDAQ: SFIX) Stitch Fix is redefining retail by combining human creativity with advanced data science and Generative AI. As we build the future of personalized shopping, we're equally committed to building yours. We believe in investing in our team as much as our technology. Join us to be a trendsetter in the industry and help us redefine what's possible for our clients, while we help you reach your full potential.
About the Role
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The Client Experience Product Algorithms team is responsible for the machine learning, AI, experimentation, and product analytics capabilities that power personalized experiences for Stitch Fix clients and stylists. Partnering across Product, Engineering, Design, Styling, Marketing, Merchandising, Finance, Enterprise Analytics, Data Platform, and DSN, the team translates data and algorithms into measurable business impact.
As Director, Product Algorithms, you will lead the strategy, execution, and people behind our Growth, Styling, and Fix \& Freestyle Algorithms portfolios. You'll define how AI, machine learning, experimentation, and analytics shape the future of personalized shopping while building the operating discipline, technical excellence, and cross\-functional alignment needed to deliver scalable business results.
Responsibilities
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- Lead the Product Algorithms portfolio across Growth, Styling, and Fix \& Freestyle, setting strategy and driving measurable outcomes across acquisition, engagement, retention, styling quality, Fix, Freestyle, outfitting, and related commerce experiences.
- Define the vision and roadmap for applying data science, machine learning, AI, experimentation, and product analytics to improve client experiences, stylist effectiveness, and business performance.
- Drive innovation by identifying, evaluating, and scaling modern AI, machine learning, personalization, and experimentation techniques that create meaningful impact while balancing technical feasibility, execution capacity, and business priorities.
- Strengthen organizational effectiveness by improving planning, prioritization, experimentation practices, product analytics capabilities, operational readiness, incident management, and cross\-functional decision making across the portfolio.
- Build and develop a high\-performing organization of managers and senior individual contributors, fostering technical excellence, strong product leadership, accountability, and collaborative partnerships while representing Product Algorithms with executive stakeholders.
How AI and Tools Show Up in Your Day\-to\-Day Work
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- Use AI\-powered tools (such as ChatGPT, GitHub Copilot, and other emerging technologies) to improve decision making, productivity, communication, and operational efficiency.
- Leverage experimentation platforms, analytics tools, machine learning infrastructure, and product development systems to guide roadmap decisions and measure business impact.
- Continuously evaluate and adopt new AI capabilities, technologies, and ways of working that improve team effectiveness and accelerate innovation.
About You
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You are a strategic technical leader who combines deep expertise in machine learning and AI with exceptional product thinking, organizational leadership, and business judgment. You thrive in highly collaborative environments, enjoy solving ambiguous problems, and know how to translate emerging technologies into measurable customer and business outcomes.
Requirements
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- Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, Mathematics, or a related quantitative field; advanced degree preferred.
- 10\+ years of experience in data science, machine learning, experimentation, product analytics, AI, or algorithmic product development, including 5\+ years leading teams and experience managing managers or senior technical leaders.
- Demonstrated success defining strategy and delivering production machine learning, AI, personalization, or experimentation capabilities that drive measurable product and business outcomes.
- Strong technical fluency across modern machine learning, generative AI/LLMs, experimentation methodologies, product analytics, and production algorithmic systems.
- Experience building cross\-functional partnerships with Product, Engineering, Design, Analytics, Data Platform, Marketing, Merchandising, Finance, and executive stakeholders.
- Exceptional strategic thinking, communication, prioritization, problem\-solving, and organizational leadership skills, with the ability to influence across technical and non\-technical audiences.
- Experience using AI tools (such as ChatGPT, GitHub Copilot, or similar technologies) to improve productivity, decision making, collaboration, and the quality of work.
- This position requires the ability to sit for extended periods of time, use a computer and standard office equipment, and communicate effectively in both written and verbal formats.
Nice to Have
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- Advanced degree (MS or PhD) in Computer Science, Machine Learning, Statistics, Operations Research, Engineering, or a related field.
- Experience leading AI\- or ML\-driven consumer products within e\-commerce, marketplaces, retail, recommendations, personalization, or digital consumer experiences.
- Experience building or evolving experimentation platforms, product analytics capabilities, or AI governance and operational excellence practices.
- Familiarity with large\-scale recommendation systems, ranking models, generative AI applications, causal inference, optimization, or reinforcement learning.
- Experience leading organizations through significant technical transformation, organizational scaling, or AI adoption initiatives.
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Salary Context
This $225K-$282K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Stitch Fix, 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($253K) sits 18% above the category median. Disclosed range: $225K to $282K.
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.
Stitch Fix AI Hiring
Stitch Fix has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $282K - $282K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 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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