Senior ML Engineer, Search Optimization (Agentic Search)

The Product

In a rapidly evolving world, trust in AI depends on AI agents being grounded in fresh, verified real-world data. Search is the foundation that makes this possible.

We are building an agent-native search platform designed specifically for AI systems rather than human users. Our product provides programmatic, low-latency, and observable search APIs that AI agents use to retrieve, filter, and reason over real-world information at scale.

The Role

As a Senior ML Engineer focused on Search Optimization, you will work on improving the quality of our search product across the all parts of the search stack.

• You will work on problems such as query understanding, query reformulation and expansion, retrieval, ranking, reranking, and result selection. Your goal will be to identify where search quality is lost, develop better approaches, and turn them into measurable improvements in production.
• This is an applied ML and information-retrieval role combining experimentation with production impact. You will work with real-world queries, large-scale search systems, and evaluation signals to improve relevance, recall, freshness, and overall result quality.

In this position, your responsibility will be to

Design, implement, and operate the retrieval system for a search vertical

Connect and tune the data pipeline, from ingestion to relevance tuning

Build knowledge-graph and entity-resolution layers: entity linking / NER , ontologies, and graph databases (Neo4j or similar)

Develop structured-extraction pipelines over messy, unstructured domain data

Reason about freshness and trust: model how confident we are in a fact and how stale it has become before we serve it

Define evaluation and quality metrics for relevance and drive measurable improvements

Collaborate with crawling, indexing, and ML teams to ensure retrieval and ranking requirements are met

Enable safe experimentation with retrieval, ranking, and extraction strategies

You may be a good fit if you have:

6+ years of software engineering experience, some of it in search / information retrieval

Strong IR fundamentals: inverted indexes, BM25 / TF-IDF , query understanding, ranking, and evaluation (nDCG/ MRR /recall@k)

Experience with vector & hybrid retrieval: ANN, dense+sparse fusion, embeddings models

Experience building structured extraction over messy/unstructured domain data

Fluent in Python and comfortable with systems-level performance work

Strong candidates may also have experience with:

Knowledge graphs: entity resolution, entity linking / NER , graph DBs (Neo4j), ontologies / schema design

Owning relevance / ranking for a real product and improving it against IR metrics

Data quality, truth discovery, or systems that decide how much to trust a piece of information

Published work on IR , ranking, or knowledge graphs