Applied AI Researcher
Role Overview
This Applied AI Researcher role involves deploying cutting-edge techniques in agentic workflows, web infrastructure, and RAG to transform ideas into production-ready systems. As a senior-level position, the hire will develop benchmarks, productionize research, and build reliable systems, contributing to foundational layers in the agent stack and shaping internal and public research documentation.
Perks & Benefits
This is a fully remote position, likely with flexible hours, though time zone expectations may align with team collaboration. The role offers opportunities for career growth through applied research, public writing or talks, and working in a fast-paced startup environment, fostering a culture of innovation and staying updated on academic and open-source developments.
Full Job 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.
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