Staff Applied AI Researcher (Agentic Search)
pbThis position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Applied AI Researcher (Agentic Search) based in Italy. /b /p pIn 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. /p br/ h3Accountabilities /h3 pAs 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. /p ul lipLead applied AI research and define the technical direction of retrieval and ranking systems. /p /li lipDesign and evolve multi-stage retrieval architectures covering query understanding, query rewriting, retrieval, reranking, and iterative retrieval. /p /li lipDevelop agent-native search capabilities supporting both low-latency, single-step queries and complex research tasks involving iterative planning, querying, and reasoning. /p /li lipLead experimentation with advanced retrieval techniques, including embedding research, sparse and hybrid retrieval, reranking, and context compression. /p /li lipTrain and optimize ranking and retrieval models specifically designed for agent-driven workflows and real-world production constraints. /p /li lipDevelop approaches for grounding large language models (LLMs) in real-time web data at scale, incorporating trust,
quality, and freshness signals to identify reliable sources. /p /li lipCollaborate with evaluation teams to develop new metrics and assessment frameworks for agentic systems, moving beyond traditional click-based performance indicators. /p /li lipAnalyze and optimize trade-offs between search relevance, system latency, operational reliability, and computational costs. /p /li lipPartner closely with engineering teams to deploy, scale, and maintain advanced AI search capabilities across production workloads. /p /li lipTranslate the latest advances in AI and machine learning research into practical solutions with measurable production impact. /p /li lipMentor researchers and engineers, promote technical excellence, and contribute to the team’s long-term research strategy. /p /li lipTake ownership of a key component of the search stack, such as ranking or iterative retrieval, and drive its development throughout the first year. /p /li /ul h3Requirements /h3 pYou 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. /p ul lipAt least 8 years of experience in applied AI, machine learning, or software engineering. /p /li lipDemonstrated track record of designing, developing, and deploying machine learning or AI systems at scale. /p /li lipDeep expertise in information retrieval, search technologies, ranking systems, or Retrieval-Augmented Generation (RAG) assistants. /p /li lipStrong understanding of modern deep learning techniques, particularly transformer architectures and embedding models. /p /li lipExperience building or developing LLM-integrated systems and knowledge-intensive applications.
/p /li lipProven experience designing evaluation frameworks, performance metrics, and experimentation methodologies for machine learning systems. /p /li lipStrong programming skills in Python and the ability to build robust, scalable technical solutions. /p /li lipPublications in leading AI and machine learning conferences, such as ICLR, ICML, NeurIPS, AAAI, EMNLP, or ACL. /p /li lipAbility to operate effectively in a fast-paced, product-driven environment with a high degree of ownership and autonomy. /p /li lipStrong analytical and problem-solving skills, with the ability to balance research innovation against practical engineering constraints. /p /li lipExcellent collaboration and communication skills, particularly when working across research and engineering teams. /p /li /ul h3Nice-to-Have Qualifications /h3 ul lipExperience developing agentic AI systems, including autonomous agents, tool use, and multi-step workflows. /p /li lipFamiliarity with reinforcement learning post-training techniques for large language models (LLMs) or vision-language models (VLMs). /p /li /ul h3Benefits /h3 ul lipbHigh-impact research ownership: /b Shape the applied AI research direction and influence the architecture of next-generation agentic search systems. /p /li lipbEnd-to-end technical responsibility: /b Own a critical component of the search stack and take innovations from research through production deployment. /p /li lipbAdvanced AI challenges: /b Work on cutting-edge retrieval, ranking, and reasoning problems involving real-time web data and large-scale AI workloads. /p /li lipbResearch-driven environment: /b Apply the latest advances from the global AI research community to real-world products. /p /li lipbTechnical leadership opportunities: /b Mentor researchers and engineers while helping establish high technical standards. /p /li lipbInternational work environment: /b Opportunities based in Amsterdam, London, Zurich, or remotely within Europe. /p /li lipbHigh ownership and autonomy: /b Work in a fast-moving, product-oriented environment where your research and technical decisions have a direct impact. /p /li /ul #J-18808-Ljbffr
quality, and freshness signals to identify reliable sources. /p /li lipCollaborate with evaluation teams to develop new metrics and assessment frameworks for agentic systems, moving beyond traditional click-based performance indicators. /p /li lipAnalyze and optimize trade-offs between search relevance, system latency, operational reliability, and computational costs. /p /li lipPartner closely with engineering teams to deploy, scale, and maintain advanced AI search capabilities across production workloads. /p /li lipTranslate the latest advances in AI and machine learning research into practical solutions with measurable production impact. /p /li lipMentor researchers and engineers, promote technical excellence, and contribute to the team’s long-term research strategy. /p /li lipTake ownership of a key component of the search stack, such as ranking or iterative retrieval, and drive its development throughout the first year. /p /li /ul h3Requirements /h3 pYou 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. /p ul lipAt least 8 years of experience in applied AI, machine learning, or software engineering. /p /li lipDemonstrated track record of designing, developing, and deploying machine learning or AI systems at scale. /p /li lipDeep expertise in information retrieval, search technologies, ranking systems, or Retrieval-Augmented Generation (RAG) assistants. /p /li lipStrong understanding of modern deep learning techniques, particularly transformer architectures and embedding models. /p /li lipExperience building or developing LLM-integrated systems and knowledge-intensive applications.
/p /li lipProven experience designing evaluation frameworks, performance metrics, and experimentation methodologies for machine learning systems. /p /li lipStrong programming skills in Python and the ability to build robust, scalable technical solutions. /p /li lipPublications in leading AI and machine learning conferences, such as ICLR, ICML, NeurIPS, AAAI, EMNLP, or ACL. /p /li lipAbility to operate effectively in a fast-paced, product-driven environment with a high degree of ownership and autonomy. /p /li lipStrong analytical and problem-solving skills, with the ability to balance research innovation against practical engineering constraints. /p /li lipExcellent collaboration and communication skills, particularly when working across research and engineering teams. /p /li /ul h3Nice-to-Have Qualifications /h3 ul lipExperience developing agentic AI systems, including autonomous agents, tool use, and multi-step workflows. /p /li lipFamiliarity with reinforcement learning post-training techniques for large language models (LLMs) or vision-language models (VLMs). /p /li /ul h3Benefits /h3 ul lipbHigh-impact research ownership: /b Shape the applied AI research direction and influence the architecture of next-generation agentic search systems. /p /li lipbEnd-to-end technical responsibility: /b Own a critical component of the search stack and take innovations from research through production deployment. /p /li lipbAdvanced AI challenges: /b Work on cutting-edge retrieval, ranking, and reasoning problems involving real-time web data and large-scale AI workloads. /p /li lipbResearch-driven environment: /b Apply the latest advances from the global AI research community to real-world products. /p /li lipbTechnical leadership opportunities: /b Mentor researchers and engineers while helping establish high technical standards. /p /li lipbInternational work environment: /b Opportunities based in Amsterdam, London, Zurich, or remotely within Europe. /p /li lipbHigh ownership and autonomy: /b Work in a fast-moving, product-oriented environment where your research and technical decisions have a direct impact. /p /li /ul #J-18808-Ljbffr