Staff Applied AI Researcher (Agentic Search)

Jobgether; France; Remoto; EUR 120.000 - 180.000; Tempo pieno

Vantaggi offerti da questo lavoro

High-impact research

End-to-end ownership

Advanced AI challenges

Research-driven

Leadership opportunities

International team

Autonomy

Descrizione del lavoro

Jobgether is seeking a Staff Applied AI Researcher to shape the future of AI-powered information retrieval. You will lead research across retrieval, ranking, and agentic search architectures, translating advances into production-ready systems.

You will mentor researchers and engineers, own critical components of the search stack, and collaborate with cross-functional teams across Europe. This role offers international exposure, strong ownership, and a fast-paced, product-driven environment where

Competenze

• 8+ years in applied AI, ML or software engineering.
• Experience deploying ML systems at scale.
• Deep expertise in information retrieval, search, ranking.
• Strong Python programming and production-ready systems.
• Experience with LLM-integrated systems and knowledge applications.
• Ability to design evaluation frameworks and experiments.
• Publications in AI/ML conferences preferred.
• Ability to work autonomously in fast-paced product environments.
• Strong collaboration and communication across research and engineering teams.

Mansioni

• Define research strategy for agent-native search systems.
• Lead retrieval architectures including ranking and iterative retrieval.
• Develop low-latency search capabilities for agent tasks.
• Experiment with embedding, sparse/hybrid retrieval and context compression.
• Train and optimize ranking models for agent-driven workflows.
• Ground LLMs in real-time web data with trust signals.
• Create evaluation metrics for agentic systems and production impact.
• Balance relevance, latency, reliability and cost in systems.
• Collaborate with engineering to deploy AI search at scale.
• Mentor researchers and engineers and shape long-term strategy.
• Own a key component of the search stack in the first year.

Conoscenze

Applied AI research

Machine learning engineering

Information retrieval

Python programming

Experimentation

LLMs integration

Collaboration

Research leadership

Production deployment

Descrizione del lavoro

In this role, you will shape the future of AI-powered information retrieval by developing next-generation search systems designed specifically for autonomous AI agents. You will lead applied research and technical innovation across retrieval, ranking, and agentic search architectures. Your work will enable AI systems to plan, retrieve, evaluate, and refine information from the web in real time. Operating at the intersection of advanced machine learning research and large-scale production engineering, you will tackle complex challenges involving relevance, latency, reliability, and cost. You will take ownership of a critical part of the search infrastructure, translating cutting-edge research into measurable product improvements. This is an opportunity to influence the technical direction of a rapidly evolving AI platform while mentoring talented researchers and engineers.

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.
• Research-driven environment: Apply the latest advances from the global AI research community to real-world products.
• Technical leadership opportunities: Mentor researchers and engineers while helping establish high technical standards.
• International work environment: Opportunities based in Amsterdam, London, Zurich, or remotely within Europe.
• High ownership and autonomy: Work in a fast-moving, product-oriented environment where your research and technical decisions have a direct impact.

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