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genai vector search retrieval ranking embeddings📋 Description
- Be a founding MLE, owning key parts of retrieval or ranking as the team grows.
- Design, build, and deploy hybrid search systems (ML + genAI + rules) for domain search.
- Lead direction in retrieval (index design, query parsing) or ranking (learning-to-rank).
- Lead embedding model selection, post-training optimization, and evaluation.
- Collaborate to translate product ideas into technical approaches (build vs buy).
🎯 Requirements
- 7+ years of ML engineering experience, with 2+ years in search, retrieval, ranking, or recommendations.
- Experience building production search or ranking systems end-to-end.
- Depth in two or more areas: retrieval, ranking, or embeddings; LLM integration a plus.
- Ability to own strategy and shape roadmaps.
- Strong architectural judgment; communicates tradeoffs to technical and non-technical stakeholders.
- Proven ability deploying and operating ML systems at scale.
🎁 Benefits
- A choice between medical plans with an option for 100% covered premiums
- Fertility and adoption benefits
- Access to supplemental insurance plans for additional coverage
- Headspace mindfulness app subscription
- Global Employee Assistance Program
- Retirement benefits with employer match
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