Backend Engineer - Recommendation (all genders)

Our mission is to digitize the classic shopping stroll, turning it into a personalized, inspiring experience for every customer. Technology and data sit at the center of everything we build. We are not a fashion company with a tech team on the side. Tech drives our product, our strategy, and our growth.
Our proprietary tech stack powers our own shops and SCAYLE, our commerce infrastructure solution that serves over 350 brands globally. Same foundation, bigger stage. The challenges keep things interesting.

• Develop the recommendation engine powering ABOUT YOU's homepage, product, and outfit pages in Node.js/TypeScript
• Serve offline-trained ML models (matrix factorization, brand similarity, image embeddings) at Black-Friday scale from Valkey, DynamoDB, and OpenSearch
• Design personalization logic: feed assembly, fallback chains, deduplication, diversity, and A/B experimentation
• Build GenAI product features like our streaming Gemini fashion advisor, focusing on low latency, session state, and token cost control
• Own reverse-ETL pipelines that move BigQuery model outputs into high-performance hot stores
• Instrument recommendation coverage, track business metrics via SQS/BI systems, and shape architecture alongside our Tech Lead

• You take end-to-end responsibility for our recommendation engine and love seeing your changes directly impact customer engagement
• 3+ years of experience building high-throughput backend services (Node.js/TypeScript preferred, or fast-converting from Java, Go, or Kotlin)
• Real depth in Redis/Valkey data modeling, caching hierarchies (multi-tier, TTLs/invalidation), and binary payloads (Protobuf/Avro)
• High-throughput mindset: comfortable operating APIs that scale to 10k+ rps under strict latency SLOs
• You're excited to bridge backend engineering and ML by serving model scores, embeddings, or LLM features in live systems
• Clear communication skills in English and a data-driven, experiment-friendly mindset

Nice to have

• RecSys fundamentals (collaborative filtering, matrix factorization, cold-start handling, diversity algorithms)
• Streaming GenAI / LLM product experience (chat UX, prompt caching, guardrails)
• Experience with AWS serverless (DynamoDB modeling, SQS/SNS) and OpenSearch k-NN indices
• Performance profiling (clinic.js, flamegraphs) and load testing (k6)