Accountabilities

As a Staff Applied AI Researcher, you will drive research strategy and technical development across agent-native search systems, ensuring that innovative approaches deliver reliable results in demanding production environments.

Lead applied AI research and define the technical direction of retrieval and ranking systems.

Design and evolve multi-stage retrieval architectures covering query understanding, query rewriting, retrieval, reranking, and iterative retrieval.

Develop agent-native search capabilities supporting both low-latency, single-step queries and complex research tasks involving iterative planning, querying, and reasoning.

Lead experimentation with advanced retrieval techniques, including embedding research, sparse and hybrid retrieval, reranking, and context compression.

Train and optimize ranking and retrieval models specifically designed for agent-driven workflows and real-world production constraints.

Develop approaches for grounding large language models (LLMs) in real-time web data at scale, incorporating trust, quality, and freshness signals to identify reliable sources.

Collaborate with evaluation teams to develop new metrics and assessment frameworks for agentic systems, moving beyond traditional click-based performance indicators.

Analyze and optimize trade-offs between search relevance, system latency, operational reliability, and computational costs.

Partner closely with engineering teams to deploy, scale, and maintain advanced AI search capabilities across production workloads.

Translate the latest advances in AI and machine learning research into practical solutions with measurable production impact.

Mentor researchers and engineers, promote technical excellence, and contribute to the team's long-term research strategy.

Take ownership of a key component of the search stack, such as ranking or iterative retrieval, and drive its development throughout the first year.

Requirements

You are an experienced applied AI researcher or machine learning engineer with a strong research background and a proven ability to turn sophisticated technical ideas into production-ready systems.

At least 8 years of experience in applied AI, machine learning, or software engineering.

Demonstrated track record of designing, developing, and deploying machine learning or AI systems at scale.

Deep expertise in information retrieval, search technologies, ranking systems, or Retrieval-Augmented Generation (RAG) assistants.

Strong understanding of modern deep learning techniques, particularly transformer architectures and embedding models.

Experience building or developing LLM-integrated systems and knowledge-intensive applications.

Proven experience designing evaluation frameworks, performance metrics, and experimentation methodologies for machine learning systems.

Strong programming skills in Python and the ability to build robust, scalable technical solutions.

Publications in leading AI and machine learning conferences, such as ICLR, ICML, NeurIPS, AAAI, EMNLP, or ACL.

Ability to operate effectively in a fast-paced, product-driven environment with a high degree of ownership and autonomy.

Strong analytical and problem-solving skills, with the ability to balance research innovation against practical engineering constraints.

Excellent collaboration and communication skills, particularly when working across research and engineering teams.

Nice-to-Have Qualifications

Experience developing agentic AI systems, including autonomous agents, tool use, and multi-step workflows.

Familiarity with reinforcement learning post-training techniques for large language models (LLMs) or vision-language models (VLMs).

Benefits

High-impact research ownership: Shape the applied AI research direction and influence the architecture of next-generation agentic search systems.

End-to-end technical responsibility: Own a critical component of the search stack and take innovations from research through production deployment.

Advanced AI challenges: Work on cutting-edge retrieval, ranking, and reasoning problems involving real-time web data and large-scale AI workloads.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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