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Applied AI Researcher at Tavily
Tel Aviv, Tel-Aviv District, Israel -
Full Time

Start Date

Immediate

Expiry Date

04 Apr, 26

Salary

0.0

Posted On

04 Jan, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Applied Research, LLMs, RAG Pipelines, Autonomous Agent Architectures, Benchmarks, Evaluation Frameworks, Productionization, Prototyping, Open-source Libraries, Experimentation, Data Analysis, Iterative Development

Industry

technology;Information and Internet

Description
As an Applied AI Researcher at Tavily, you’ll explore and deploy cutting-edge techniques in agentic workflows, web infrastructure, and RAG—transforming ideas into production-ready systems. This role is ideal for someone passionate about applied research and eager to shape one of the most foundational layers in the agent stack. What You’ll Do: Be the expert in the latest research and tooling around LLMs, RAG pipelines, and autonomous agent architectures. Develop benchmarks and evaluation frameworks. Productionize research and build end-to-end reliable systems and models. Contribute to internal research documentation and, when appropriate, public writing or talks. Stay up to date on relevant academic and open-source developments in the field. What We’re Looking For: M.Sc. in Computer Science, Data Science, or a related technical field — or B.Sc. with 3+ years of hands-on experience in AI/ML applications. Strong understanding of LLMs, embeddings, cross-encoders, vector search, and evaluation techniques. Experience running experiments and analyzing results with rigor. Ability to translate ideas into prototypes and iterate quickly. Familiarity with open-source libraries, models and research environments. Nice to Have: PhD in Computer Science, or a related technical field. Experience in a fast-paced startup. Paper publications in conferences.
Responsibilities
As an Applied AI Researcher, you will explore and deploy advanced techniques in agentic workflows and web infrastructure. You will also develop benchmarks, productionize research, and contribute to internal documentation.