Senior Staff Machine Learning Architect, Personalization

Experteer Overview As Architect for the recommender system, you will define the long-term technical strategy and set architectural direction across retrieval, ranking, reranking, and real-time serving. You will partner with engineering, data science, and product teams to ensure scalable personalization that balances relevance with business outcomes. You will influence cross-team investments and mentor others while remaining hands-on with research, prototyping, and production issues. This role offers a senior IC path with broad impact on ThredUp’s shopping experience. You will help shape a data-driven, scalable platform at a mission-driven company focused on sustainable fashion. Compensation / Benefits * Define and own the end-to-end architecture for recommendation systems across retrieval, ranking, reranking, and real-time serving * Coordinate across teams to influence investment decisions by authoring architecture RFCs and leading technical reviews * Design for scale: low-latency serving, high-throughput retrieval, real-time feature freshness for millions of items * Collaborate with product and business stakeholders to translate goals into ML system requirements * Mentor across teams to raise the technical bar * Engage hands-on: study current research, prototype, review designs, and address production issues Tasks * 10+ years in Data Science, ML Engineering, or Software Engineering, with 5+ years in recommender systems * MS or PhD in Computer Science, Data Science, Engineering, or related quantitative field; or equivalent experience * Proven ability to architect end-to-end recommender pipelines spanning multiple services * Deep expertise in ML models for retrieval, ranking, and reranking * Track record of setting technical direction across teams or product lines * Experience balancing ML metrics (recall, precision, NDCG) with business metrics (conversion, AOV) * Knowledge of distributed systems at scale: low-latency serving, feature stores, vector databases, streaming pipelines * Excellent written and verbal communication; able to align technical and non-technical stakeholders Key requirements * 4-day work week * Hybrid work environment * Employee stock purchase plan * Discretionary RSU awards * Flexible PTO + 13 holidays * Paid Sabbatical after 3 years

NDCG) high-throughput retrieval, real-time feature freshness for millions of items * Collaborate with product and business stakeholders to translate goals into ML system requirements * Mentor across teams to raise the technical bar * Engage hands-on: study current research, prototype, review designs, and address production issues Tasks * 10+ years in Data Science, ML Engineering, or Software Engineering, with 5+ years in recommender systems * MS or PhD in Computer Science, Data Science, Engineering, or related quantitative field; or equivalent experience * Proven ability to architect end-to-end recommender pipelines spanning multiple services * Deep expertise in ML models for retrieval, ranking, and reranking * Track record of setting technical direction across teams or product lines * Experience balancing ML metrics (recall, precision, NDCG) with business metrics (conversion, AOV) * Knowledge of distributed systems at scale: low-latency serving, feature stores, vector databases, streaming pipelines * Excellent written and verbal communication; able to align technical and non-technical stakeholders Key requirements * 4-day work week * Hybrid work environment * Employee stock purchase plan * Discretionary RSU awards * Flexible PTO + 13 holidays * Paid Sabbatical after 3 years