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About ThredUp
ThredUp is transforming resale with technology and a mission to inspire the world to think secondhand first. By making it easy to buy and sell secondhand, ThredUp has become one of the world's largest online resale platforms for apparel, shoes and accessories. Sellers love ThredUp because we make it easy to clean out their closets and unlock value for themselves or for the charity of their choice while doing good for the planet. Buyers love shopping value, premium and luxury brands all in one place, at up to 90% off estimated retail price. Our proprietary operating platform is the foundation for our managed marketplace and consists of distributed processing infrastructure, proprietary software and systems and data science expertise. With ThredUp’s Resale\-as\-a\-Service, some of the world's leading brands and retailers are leveraging our platform to deliver customizable, scalable resale experiences to their customers. ThredUp has processed over 172 million unique secondhand items from 55,000 brands across 100 categories. By extending the life cycle of clothing, ThredUp is changing the way consumers shop and ushering in a more sustainable future for the fashion industry.
Recognized on TIME Most Influential Companies of 2023, Digiday's WorkLife 50 2023, TIME's Best Inventions of 2022, and Lattice's People Success Awards 2022\.
How You Will Make An Impact
We're looking for an Architect to define the technical strategy for our recommender system as we seek to personalize the experience for everyone on the ThredUp platform. In this role, you'll set the architectural direction that our personalization teams build on, and you'll be a key technical advisor to other teams shaping the data, backend, and frontend systems that power our recommender system.
You'll partner closely across engineering leadership, data science, and product to make sure our recommendation infrastructure scales with the business, balancing relevance, diversity, and business outcomes like conversion and AOV, while staying flexible enough to adapt to emerging product innovations.
This is a senior individual contributor role with a major opportunity to directly impact company performance. You won't have direct reports, but you'll drive impact by shaping how dozens of engineers and data scientists build the next era of personalized shopping experiences at ThredUp.
In This Role You’ll Get To
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- Define and own the long\-term technical architecture for recommendation systems spanning retrieval, ranking, re\-ranking, and real\-time serving across multiple surfaces.
- Act as the connective tissue across teams that build and operate recommender system components and influence investment decisions across the org by authoring architecture RFCs and leading technical reviews
- Design for scale: low\-latency serving, high\-throughput candidate retrieval, real\-time feature freshness, and a catalog of millions of unique items listed at any point in time
- Partner with product and business stakeholders to translate merchandising, catalog, and revenue goals into ML system requirements.
- Mentor across teams; raise the technical bar company\-wide
- Be hands\-on: read the latest recommender system research, prototype, review code and designs, and dig into production issues when the problem calls for it.
What We're Looking For
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- 10\+ years in Data Science, Machine Learning Engineering, or Software Engineering, including 5\+ years focused specifically on recommender systems.
- MS or PhD in Computer Science, Data Science, Engineering, or a related quantitative field; or equivalent industry experience.
- We are open to considering candidates with prior position titles and experience as an Applied Scientist, Software Engineer, or Data Scientist.
- Demonstrated experience architecting end\-to\-end recommender system pipelines that span multiple services. Deep expertise in machine learning models used across retrieval, ranking, and reranking.
- A track record of setting technical direction across teams or product lines. Examples include: architecture decisions adopted org\-wide, cross\-team platform migrations, or serving as a technical authority across multiple engineering groups.
- Experience balancing ML metrics (recall, precision, NDCG) against business metrics (conversion, AOV, etc.).
- Knowledge of how to build distributed systems at scale: low\-latency serving, feature stores, vector databases, and streaming pipelines.
- Excellent written and verbal communication. Able to drive alignment across technical and non\-technical stakeholders without formal authority.
- Experience building highly adaptive recommender systems, typically involving some aspect of real\-time feature generation and reinforcement learning is a plus.
- Experience working with large product catalogs with a high degree of cold start problems; this may but need not be in an eCommerce setting is a plus.
- Hands\-on experience working with OpenSearch is a plus.
- Open\-source contributions or publications in recommender systems, retrieval, or ranking is a plus.
At ThredUp, your base pay is one part of your total compensation package. This role pays between $200,000 and $230,000, and your actual base pay will depend on your skills, qualifications, experience, and location.
Many ThredUp employees also have the opportunity to own shares of ThredUp stock, ThredUp employees are eligible for discretionary restricted stock unit awards, as well as a discount when purchasing ThredUp stock if voluntarily participating in ThredUp’s Employee Stock Purchase Plan. Subject to eligibility requirements, you’ll also receive other benefits: Comprehensive medical \& dental coverage, vision, 401k, life and disability insurance.
This role is not eligible for visa sponsorship.
What We Offer:
- 4\-day work week, with Fridays off
- Hybrid work environment: 3 days in the office and 1 day remote each week
- Competitive salary (we leverage market data)
- Many ThredUp employees also have the opportunity to own shares of ThredUp stock and are eligible for discretionary restricted stock unit awards
- Employee stock purchase plan
- Flexible PTO (take the time you need) \+ 13 company holidays
- Paid Sabbatical after 3 years of full time employment
- Generous paid parental leave for new mothers and fathers
- Medical, dental, vision, 401k, life and disability insurance offered
- We live by our Core Values of Transparency, SpeakingUP, Thinking Big, Infinite Learning, Influencing Outcomes \& Seeking the Truth
We believe diversity, inclusion and belonging is key for our team
At ThredUp, our mission has been built on extending the lives of millions of unique clothing items. Much like our inventory, we are proud to have fostered a workplace that is one\-of\-a\-kind. As a company focused on diversity, inclusion and belonging, we are committed to ensuring our employees are comfortable bringing their authentic selves to work every day. A unique perspective is critical to solving complex problems and inspiring a new generation to think secondhand first. Be you.
If you are a candidate with a disability and have a reasonable accommodation request for the job application process, please email *[email protected]* the specific details of your disability related accommodation request. This email address is reserved for candidates with disabilities only. General application inquiries will not receive a response.
Salary Context
This $200K-$230K range is above the median 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 ThredUp, 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. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $200K to $230K.
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.
ThredUp AI Hiring
ThredUp has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Oakland, CA, US. Compensation range: $230K - $230K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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