Senior ML Engineer, Search Optimization (Agentic Search)

Tavily is hiring a Senior ML Engineer, Search Optimization (Agentic Search) in Tel Aviv, Israel. Level rates it ; you can apply on Tavily.

Level's read on this job

On Level's scale, this machine learning engineer role is Builds AI ●●●●: the job is building AI systems.
The posting mentions AI agents among its tools and skills.
Level classes it as a senior role.
Tavily has 13 other open roles on Level.
Posted in the last few days.

AI in this role

ai-agents

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

How we rate this

Senior ML Engineer, Search Optimization (Agentic Search) at Tavily rates 97 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior ML Engineer, Search Optimization (Agentic Search) is an open job at Tavily.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Tel Aviv, Israel.
The job was posted on November 4, 2026.
Seniority: senior.
Applications are handled by Tavily. Apply on Tavily

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agents

Also common in machine learning engineer jobs at this level

Machine learning PyTorch LLM TensorFlow Deep learning AWS

Pay

Typical machine learning engineer job: $200k

This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs

Questions you could be asked

How do you decide when an AI agent can act on its own versus asking for approval first?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: AI agents. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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