Machine Learning Engineer, Stitchfix
Recommendation Algorithms and Foundational Models
$178.2-262k
+ Restricted stock units
Online personal styling
Be an early applicant
Online personal styling
1001+ employees
Be an early applicant
$178.2-262k
+ Restricted stock units
1001+ employees
Company mission
To change the way people find clothes they love by combining technology with the personal touch of seasoned style experts.
Role
Who you are
- You have 5+ years of experience in building high-throughput, resilient backend systems, with demonstrated expertise in deploying and maintaining machine learning models in production
- You have 5+ years of advanced python development experience, including familiarity with data manipulation using spark and SQL
- You have a solid foundation in machine learning, plus a bonus of evaluating recommender systems
- You are familiar with modern ML frameworks (e.g. PyTorch, TensorFlow) and model deployment frameworks leveraging container orchestration
- You have hands-on experience with observability tools (e.g., Datadog, Prometheus) and a deep understanding of how to measure and optimize system latency
- You excel at translating complex technical concepts into clear, actionable insights for cross-functional partners, including Product, Design, and Engineering, to build consensus and drive projects forward
- You are driven by a deep curiosity to explore new technologies and take calculated risks, with a focus on identifying and implementing best practices that elevate the team's engineering standards
What the job involves
- At Stitch Fix, we are at the forefront of innovation, creating cutting-edge solutions that blend fashion, technology, and data science
- Our data science team combines machine learning with expert human judgment to generate innovative recommendations and insights that transform the way our clients discover what they love
- We believe in a curiosity-driven data science culture where members are empowered to deliver impact through end-to-end model development
- The diversity of the problems that we work on and the data-rich environment of our business make it possible, even essential, to bring the tools of multiple disciplines to bear on our hardest problems
- Multiple teams are looking for machine learning engineers. Here are the teams you will have the opportunity to join:
- Foundational Models: This team focuses on developing the core recommender system, which not only personalizes the client experience but also informs merchandising strategies. The team’s goal is to enhance business outcomes by improving model performance while ensuring optimal latency, uptime, and cost efficiency
- Recommendation Algos: This team leverages personalization algorithms to power the Fix experience, which involves a combination of modeling style, diversity, long-term client behavior, and expanding Stitch Fix’s total addressable market. In doing so, this team builds assortments that delight our clients while at the same time explore a client’s stylistic preferences and drive client engagement
- In this role, you will be building the next generation ML systems that transform the way people find what they love. As a Machine Learning Engineer, your focus will be on building, productionizing and scaling of machine learning models, and promoting engineering excellence
- You will get the opportunity to work with data scientists, as well as lead work cross-functionally with Strategy, Product, Merchandising, Marketing, and Engineering partners to build and enhance those capabilities
- Bring your engineering excellence to improve how we serve real time recommendations in production at scale
- Make a substantial impact on personalization across multiple dimensions by creating the infrastructure that allows us to build the next generation of recommendation systems
- Collaborate with data scientists on a variety of sophisticated machine learning algorithms
- Work closely with partners in ML Platform Eng, Engineering, Product and Merchandising teams
- Be empowered to take initiative and follow your innate curiosity
Our take
Shopping online for clothes is not always a fun experience. Finding clothes you like is only half the battle. Sizing guides and return policies seem to have been designed to be challenging to understand, and can also vary wildly from company to company.
Stitchfix sets out to change this by making online clothes shopping personalized and hassle-free. Customers take a survey on their personal tastes, and pay a one-off fee for a stylist, to receive a box of 5 personalized items. Then, they simply pay for any items they keep, or return what they don't want free of charge. The stylists fee is deducted from the first purchase and if they keep all 5 items, they receive a discount.
Stitchfix's unique blend of technology, and human touch in styling, allows the company to deliver its personalized experience at scale, with its growing data sets putting them in a great place to predict consumer trends and preferences. Unfortunately, the company has been hit hard by the current economic climate - cutting staff back in June 2022 as well as again in early 2023. Only time will tell if this once exciting brand will be able to bounce back from this bump in the road and find success again in the future.
Steph
Company Specialist at Welcome to the Jungle
Insights
-7% employee growth in 12 months
Company
Funding (last 2 of 4 rounds)
Jun 2014
$30m
SERIES C
Oct 2013
$12m
SERIES B
Total funding: $47.6m
Company benefits
- Leadership, managing people program, career journey & other training opportunities
- Company equity for eligible roles
- Medical, dental and vision benefits
- 401(k) plan
- Unlimited vacation for qualified roles
- Competitive parental leave
- Enjoy 40% off of Fixes plus access to quarterly sample sales
- Collaborative workspaces - Open floor plan with standing desks & living room seating throughout
Company HQ
Financial District, San Francisco, CA
Leadership
Katrina Lake
(CEO)BA in Economics from Stanford. MBA from Harvard Business School. Previously 2 years as an Associate at The Parthenon Group and 2 years as an Associate at Leader Ventures.
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