Why This Job is Featured on The SaaS Jobs
Applied AI Researcher roles are increasingly central to SaaS products that rely on LLM-driven capabilities rather than static rules or traditional ML. This listing stands out for its emphasis on “agentic workflows,” web infrastructure, and retrieval-augmented generation—areas that often sit at the boundary between core platform engineering and product-facing intelligence. The on-site Israel location also signals a role embedded closely with an engineering function, where iteration cycles and deployment decisions tend to be tightly coupled.
From a SaaS career perspective, the most durable value here is the combination of evaluation rigor and productionisation. Building benchmarks and frameworks is the kind of work that becomes a reusable skill across AI-enabled SaaS: it sharpens judgment around reliability, latency, cost, and failure modes. Translating research into end-to-end systems also develops the practical instincts needed to move from promising demos to maintainable features.
This role fits professionals who enjoy operating as a bridge between research literature and shipping software, and who prefer work that mixes experimentation with engineering discipline. It will suit someone motivated by defining “what good looks like” for LLM systems, and comfortable documenting decisions and learnings for broader teams.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Employment Type
Full time
Deadline to Apply
January 31, 2026 at 5:00 PM EST
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.
