Staff / Principal Applied AI Researcher (Agentic Search) in Winit Germany GmbH, Bremen at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff / Principal Applied AI Researcher (Agentic Search) based in Germany.

This is a senior applied research opportunity focused on redefining how AI systems access, retrieve, and reason over information from the web. You will help build an agent-native search platform designed for machines rather than traditional human search experiences. The role combines cutting-edge research with hands-on system design and a strong expectation of delivering measurable impact in production. You will work on retrieval, ranking, query understanding, LLM grounding, and evaluation for complex multi-step agent workflows. Your work will operate under demanding requirements for relevance, reliability, latency, and cost at significant scale. With substantial ownership over research direction and technical architecture, you will help shape foundational capabilities for the next generation of AI systems.

• Drive applied research and technical direction across retrieval, ranking, and agentic search systems.
• Design and evolve multi-stage retrieval architectures covering query understanding, query rewriting, reranking, iterative retrieval, and result refinement.
• Develop approaches that enable LLMs and AI agents to retrieve, evaluate, and reason over constantly changing web data in real time.
• Design agent-native retrieval systems optimized for machine consumption and complex downstream AI workflows rather than traditional search-engine user experiences.
• Build systems in which LLMs can iteratively plan, query, refine, evaluate, and reason over retrieved information.
• Develop retrieval and ranking approaches capable of supporting multi-step, agent-driven workflows under real-world production constraints.
• Define new evaluation frameworks, benchmarks, and metrics for agentic systems, recognizing that quality and correctness cannot be measured solely through traditional engagement signals.
• Lead experimentation with modern retrieval technologies, including embeddings, hybrid search, reranking, and other advanced information-retrieval approaches.
• Translate successful research into production systems in close collaboration with engineering teams.
• Analyze and optimize trade-offs between relevance, latency, reliability, and infrastructure cost at scale.
• Own ambiguous and technically challenging problems from initial research through implementation, evaluation, and production deployment.
• Contribute to broader product and research strategy, helping determine technical priorities and future directions.
• Mentor engineers and researchers, share technical knowledge, and help raise the overall technical standards of the team.

• 8+ years of professional experience in applied AI, machine learning, software engineering, or a closely related technical field.
• Proven track record of designing and shipping machine learning or AI systems into production at significant scale.
• Deep expertise in search, information retrieval, ranking, recommendation systems, AI assistants, or related areas.
• Strong understanding of modern deep learning techniques, particularly transformers, embeddings, and LLM-based systems.
• Hands-on experience developing LLM-integrated, knowledge-intensive, retrieval-based, or similar AI systems.
• Experience designing evaluation frameworks, benchmarks, and metrics for machine learning or AI systems.
• Strong programming skills in Python and proficiency in at least one additional systems-oriented language such as Go, C++, or a comparable language.
• Ability to operate effectively in a fast-moving, product-oriented environment with significant ownership, autonomy, and ambiguity.
• Strong research and problem-solving capabilities, with the ability to turn novel ideas into measurable technical improvements.
• Excellent communication and collaboration skills, particularly when working across research, engineering, and product disciplines.
• Experience with large-scale search or recommendation systems is highly desirable.
• Background in agentic AI, including AI agents, tool use, autonomous workflows, or multi-step reasoning systems, is a strong advantage.
• Experience with retrieval-augmented generation (RAG), multi-step retrieval, tool use, or related architectures is beneficial.
• Publications, open-source contributions, patents, or other evidence of significant technical depth and impact are considered a plus.

• Competitive compensation.
• Flexible working environment with significant ownership and autonomy.
• Career development and continuous learning opportunities.
• Opportunity to work on technically ambitious and high-impact AI projects.
• Exposure to cutting-edge research across agentic AI, information retrieval, LLMs, and large-scale machine learning systems.
• Opportunity to influence research direction, system architecture, and product strategy.
• Collaborative environment with highly skilled AI, engineering, and research professionals.
• International and diverse working environment.
• Opportunity to help shape foundational technology for the next generation of AI systems.
• Meaningful technical ownership and the ability to make a measurable impact in production.