Salary: $150k- $300k+ equity
Visa: US citizen
Fluency in Chinese/Mandarin is required, so only reach out exclusively to candidates of Chinese ethnic background.
JD in one line
Own recommendation, search, ranking, retrieval, and discovery for Sekai’s content ecosystem.
What you will own
- Build recommendation and search across feed, discovery, search, and content continuation.
- Own retrieval/ranking: candidate generation, embeddings, two-tower models, features, and serving quality.
- Design, launch, and analyze recommendation/search experiments.
Requirements
- 5+ years industry experience building production ML systems with senior ownership.
- Hands-on recommendation, search, ranking, ads ranking, feed ranking, or content discovery systems.
- Consumer apps, entertainment, social, gaming, creator, or engagement-driven products.
- Two-tower models, embedding retrieval, candidate generation, ranking, and online/offline evaluation.
Hard filters
- 5+ years industry experience building production ML systems with senior ownership.
- Hands-on recommendation, search, ranking, ads ranking, feed ranking, or content discovery systems.
- Consumer apps, entertainment, social, gaming, creator, or engagement-driven products.
- Two-tower models, embedding retrieval, candidate generation, ranking, and online/offline evaluation.
Strong fit
- Build recommendation and search across feed, discovery, search, and content continuation.
- Own retrieval/ranking: candidate generation, embeddings, two-tower models, features, and serving quality.
- Design, launch, and analyze recommendation/search experiments.
Bonus:
Bonus signal: 5+ years production ML
Bonus signal: recommendation systems
Bonus signal: search ranking
Bonus signal: embedding retrieval
Anti-signals
- Cannot show core Senior Machine Learning Engineer, Recommendation experience
- Not comfortable with the listed work mode
- Low ownership, coordination-only, or no shipped examples
Interview process
WeKruit screen -> Senior Machine Learning Engineer, Recommendation technical/product deep dive -> team/founder conversation.
Culture & what they’re building
- The bet: Content is moving from watched media into playable, remixable AI-generated mini apps.
- Series A, $30M raised, backed by Khosla, a16z, Mayfield, and A*
- Product sits at AI, consumer social, mobile, and interactive entertainment
- Strong bias toward AI-native builders using modern AI tools deeply
- The target company would be tiktok, insta reels, netfliex, youtube, meta, etc
- Ownership-heavy startup environment: ship fast, learn from users, shape a new category early