Applied AI Researcher (Agentic Search)

Tavily; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Tavily is seeking a Staff Applied AI Researcher to help build an agent-native search platform—an AI-driven layer over the web for agents. You will own portions of the search stack, including ranking or iterative retrieval, and drive real-world impact from research to production.

The role emphasizes designing scalable retrieval architectures, grounding LLMs in live web data, and excelling in a fast-paced, ownership-driven environment. This is a high-responsibility position in the US.

Qualifications

• 8+ years of applied AI, ML, or software engineering experience.
• Track record shipping ML/AI systems to production at scale.
• Expertise in search, retrieval, ranking or RAG-based assistants.
• Deep learning knowledge, esp. transformers and embeddings.
• Experience with LLM-integrated or knowledge-intensive systems.
• Ability to design ML evaluation frameworks and metrics.
• Strong Python programming skills.
• Publications in top venues (ICLR/ICML/NeurIPS/AAAI/EMNLP/ACL).
• High ownership in a fast-moving, product-driven environment.

Responsibilities

• Design an agent-native search system supporting low-latency single-hop and iterative deep search tasks.
• Train ranking and retrieval models for agent-driven workflows under real-world constraints.
• Bridge research to production by shipping advances to the stack.
• Develop multi-stage retrieval architectures and iteration over retrieval.
• Lead experimentation on modern retrieval approaches and productionize them.
• Ground LLMs in real-time web data using reliability signals.
• Define metrics for agentic systems and collaborate with engineers to deploy at web scale.
• Mentor researchers and engineers to raise technical standards.

Skills

8+ years experience

Shipping ML systems

Search/ranking/retrieval

Deep learning (transformers)

LLM-integrated systems

Evaluation frameworks

Python programming

Top-tier publications

Autonomy in product environment

Job description

Описание: We are seeking a Staff Applied AI Researcher to join a fast-growing team building an agent-native search platform - the web access layer for AI systems. You can think of this as Google search, but for AI agents: a system designed for machines, not humans. We are building agentic search, where AI systems actively plan, retrieve, evaluate, and refine information rather than simply returning results. As AI becomes the primary interface to the web, this layer will replace the role of traditional search engines. We are designing these systems to search and reason over web data in real time, under strict latency and reliability constraints. This means solving retrieval, ranking and passage extraction under entirely new access patterns and at significant scale, with systems operating over constantly changing, unstructured data and serving tens of thousands of production workloads 24/7. This role comes with ownership over key parts of our applied AI research direction and system design, with a strong expectation of defining new approaches and shipping measurable impact in production. In the first year, you will own a specific part of the search stack, e.g. ranking or iterative retrieval.

Задачи:

Designing an agent-native search system that handles everything from low-latency single-hop search to complex deep search tasks requiring iterative planning, querying, refinement and reasoning. Training ranking and retrieval models optimized for agent-driven workflows under real world constraints Bringing the latest advances from the research community to our search stack and ship it to production. Drive applied research and technical direction across retrieval and ranking systems Design and evolve multi-stage retrieval architectures (query understanding, rewriting, retrieval, reranking and iterative retrieval) Lead experimentation on modern retrieval approaches (embedding research, sparse and hybrid retrieval, reranking, context compression) and bring them into production Develop methods for grounding LLMs in real-time web data at scale, using trust, quality and freshness signals to surface reliable sources Partner with the evaluation team to define new metrics for agentic systems, where correctness is not reducible to clicks Analyze trade-offs across relevance, latency, and cost, and work closely with engineering to deploy systems that operate at web scale Mentor researchers and engineers and help raise the technical bar of the team

Must haves:

• 8+ years of experience in applied AI, ML, or software engineering
• Proven track record of shipping ML or AI systems to production at scale
• Deep experience with search, retrieval, ranking or building RAG-based assistants
• Strong understanding of modern deep learning, especially transformers and embeddings
• Experience with LLM-integrated or knowledge-intensive systems
• Experience designing evaluation frameworks and metrics for ML systems
• Strong programming skills in Python
• Top-tier publications (ICLR, ICML, NeurIPS, AAAI, EMNLP, ACL)
• Ability to operate in a fast-moving, product-driven environment with high ownership and autonomy

Требования:

• Background in agentic AI systems (agents, tool use, autonomous workflows)
• Experience with RL post-training of LLMs or VLMs