Senior Machine Learning Engineer, Agentic Context Retrieval and Search (Nova)
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About the role
What You'll Do
- Build retrieval across structured data (tables, SQL, dashboards, code) and unstructured content (docs, tickets, chat, media), using each source's own signals.
- Build semantic query understanding for agents: query rewriting, decomposition, intent classification, and entity resolution for multi-turn workflows.
- Build pipelines that extract entities, embeddings, summaries, and metadata from customer data, and keep them fresh.
- Build search subagents that decide what to retrieve, run multi-hop searches, check whether results are good enough, and flag when retrieval fails.
- Measure quality with ranking metrics (nDCG, MRR, Recall@K), LLM judges, human labels, and online tests. Track how retrieval affects final responses, and use the results to improve both retrieval and the harness.
- Mentor engineers on the team and help set the technical direction for Nova.
What We're Looking For
- 5+ years of hands-on MLE experience putting complex models into production at scale, including search, information retrieval, or recommendation systems.
- Master's or PhD in Computer Science, Machine Learning, AI, or a related field.
- You keep up with the latest research and focus on applying it: shipping reliable changes that measurably improve the product experience for customers.
- Deep IR expertise: lexical (BM25), dense (embeddings, ANN indexes), and hybrid retrieval, plus learning-to-rank.
- Production experience with search engines such as Elasticsearch, OpenSearch, or Vespa, including relevance tuning and reranking.
- Hands-on experience with RAG, query rewriting, reranking, and grounding techniques that reduce hallucination.
- Experience building agents on top of retrieval, such as multi-hop search or sufficiency checks.
- Strong evaluation practice: offline benchmarks, regression detection, A/B testing, and labeling pipelines.
Bonus points
- Experience with LLM APIs from multiple providers (Anthropic, OpenAI, Google), including tool calling and prompt caching.
- Experience indexing and ranking structured and unstructured data in the same system.
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