Staff Applied AI Researcher (Agentic Search)

Jobgether; España; A distancia; EUR 90.000 - 150.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Research ownership

Technical ownership

AI challenges

Research-driven environment

Leadership opportunities

International work

Autonomy

Descripción de la vacante

Jobgether is seeking an experienced Staff Applied AI Researcher to shape the future of AI-powered information retrieval. You will lead research across retrieval, ranking, and agent-native search architectures, translating cutting-edge ideas into scalable production systems.

You will mentor researchers and engineers, own critical components of the search stack, and drive measurable product improvements while collaborating across teams in a fast-paced environment.

Formación

• At least 8 years of experience in applied AI, machine learning, or software engineering.
• Proven track record of designing, deploying ML/AI systems at scale.
• Deep expertise in information retrieval and ranking systems.
• Strong knowledge of modern deep learning and transformer architectures.
• Experience building LLM-integrated systems and knowledge-intensive apps.
• Proven evaluation frameworks and experimentation methodologies.

Responsabilidades

• Define the technical direction of retrieval, ranking, and agent-native search systems.
• Design multi-stage retrieval architectures for query understanding and iterative retrieval.
• Develop agent-native search capabilities for low-latency queries and complex tasks.
• Lead experimentation with embedding, sparse, and hybrid retrieval techniques.
• Train and optimize ranking models for agent-driven workflows.
• Ground LLMs in real-time web data with trust and freshness signals.
• Collaborate with evaluation teams to create new metrics for agentic systems.
• Optimize trade-offs between relevance, latency, and cost.
• Scale and maintain AI search capabilities across production workloads.
• Translate research advances into measurable product impact.
• Mentor researchers and engineers; contribute to long-term strategy.
• Own a key component of the search stack and drive its development.

Conocimientos

Applied AI research

Machine learning

Information retrieval

Python

Transformer architectures

LLM integration

Evaluation frameworks

Experimentation methodologies

Communication skills

Collaboration

Educación

PhD in AI/ML

Herramientas

PyTorch

TensorFlow

Descripción del empleo

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.