Staff / Principal Applied AI Researcher (Agentic Search)

Jobgether; España; Presencial; EUR 120.000 - 190.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive compensation

Career growth

Autonomy

Impactful projects

International team

Diverse workplace

Descripción de la vacante

Jobgether, on behalf of a partner company, seeks a Staff / Principal Applied AI Researcher (Agentic Search) based in Spain. You will shape how intelligent agents discover, retrieve, evaluate, and reason over real-time web information, leading research across retrieval, grounding, and multi-step workflows.

You will design scalable systems that handle unstructured data, balance latency with reliability and cost, and mentor engineers while translating research into measurable production impact

Formación

• 8+ years of experience in applied AI, ML, or software engineering.
• Proven track record shipping ML/AI systems to production.
• Deep expertise in search, information retrieval, ranking, and related domains.
• Strong understanding of modern deep learning, transformers, and embeddings.
• Experience building LLM-integrated, knowledge-intensive, or retrieval-augmented systems.
• Practical experience designing evaluation frameworks and meaningful metrics for ML/AI systems.
• Strong Python and at least one systems language (Go/C++).
• Ability to work in a fast-moving, product-oriented environment with ownership and autonomy.
• Publications or open-source contributions are welcome.

Responsabilidades

• Drive applied AI research and technical direction across retrieval and ranking for agent-native search.
• Design and evolve multi-stage retrieval architectures including query understanding, rewriting, reranking, and iterative retrieval.
• Develop grounding approaches for LLMs in real-time web data while maintaining quality, scalability, and reliability.
• Build and refine systems where LLMs can plan, query, evaluate, and reason over retrieved information across multi-step workflows.
• Define new evaluation frameworks, metrics, and experimentation methodologies for agentic systems.
• Lead experimentation with modern retrieval technologies and transition successful methods into production.
• Analyze and manage trade-offs between relevance, latency, reliability, and infrastructure cost at scale.
• Partner with engineering teams to deploy AI and retrieval systems in high-throughput environments.
• Take ownership of ambiguous problems from research through implementation and contribute to broader product direction.
• Mentor engineers and raise research and engineering standards.

Conocimientos

Applied AI

ML systems

Search

Python

C++

Herramientas

Embeddings

RAG

LLMs

Descripción del empleo

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 Spain.

Join a fast-growing team building an agent-native search platform designed specifically for AI systems.
Shape how intelligent agents discover, retrieve, evaluate, and reason over real-time web information.
Lead applied AI research across retrieval, ranking, grounding, and multi-step agentic workflows.
Design systems that operate over constantly changing, unstructured data at significant scale.
Balance relevance, latency, reliability, and cost while supporting high-throughput production workloads.
Define new evaluation approaches for agentic systems where traditional search metrics are no longer sufficient.
Work closely with engineering and product teams to turn research ideas into measurable production impact.

Accountabilities

• Drive applied AI research and technical direction across retrieval and ranking systems for agent-native search.
• Design and evolve multi-stage retrieval architectures, including query understanding, query rewriting, reranking, and iterative retrieval.
• Develop approaches for grounding LLMs in real-time web data while maintaining quality, scalability, and reliability.
• Build and refine systems where LLMs can plan, query, evaluate, refine, and reason over retrieved information across multi-step workflows.
• Define new evaluation frameworks, metrics, and experimentation methodologies for agentic systems, recognizing that effectiveness cannot be measured solely through traditional click-based metrics.
• Lead experimentation with modern retrieval technologies, including embeddings, hybrid search, reranking, and related approaches, and transition successful methods into production.
• Analyze and manage trade-offs between relevance, latency, reliability, and infrastructure cost at scale.
• Partner closely with engineering teams to deploy AI and retrieval systems in high-throughput, low-latency production environments.
• Take ownership of ambiguous and complex technical problems from research through implementation and contribute to broader product and research direction.
• Mentor engineers, share technical expertise, and help raise the research and engineering standards of the team.

Requirements

• 8+ years of experience in applied AI, machine learning, or software engineering.
• Proven track record of designing and shipping ML or AI systems into production at meaningful scale.
• Deep expertise in search, information retrieval, ranking, recommendation systems, assistants, or closely related domains.
• Strong understanding of modern deep learning, particularly transformer architectures and embeddings.
• Experience building LLM-integrated, knowledge-intensive, or retrieval-augmented systems.
• Practical experience designing evaluation frameworks and meaningful metrics for machine learning or AI systems.
• Strong programming skills in Python and proficiency in at least one additional language such as Go, C++, or a comparable systems-oriented language.
• Ability to operate effectively in a fast-moving, product-oriented environment with significant ownership and autonomy.
• Strong analytical and problem-solving abilities, with the ability to translate research concepts into reliable, measurable production systems.
• Experience with large-scale search or recommendation systems is an advantage.
• Background in agentic AI, including AI agents, tool use, autonomous workflows, or multi-step reasoning systems, is a plus.
• Experience with RAG, multi-step retrieval, or tool-enabled LLM applications is beneficial.
• Publications, open-source contributions, or other evidence of technical depth and research impact are welcome.

Benefits

• Competitive compensation.
• Career growth and ongoing learning opportunities.
• High levels of flexibility, autonomy, and ownership.
• Opportunity to work on impactful applied AI projects at significant scale.
• Collaborative and innovative working environment.
• International team with highly experienced AI, engineering, and research professionals.
• Opportunity to influence technical direction and contribute to the development of emerging AI infrastructure and systems.
• Inclusive workplace with a commitment to equal employment opportunities and a diverse team environment.
• Support and reasonable accommodations throughout the application process where needed.
• Applicants must be authorized to work in the country where the position is based and may be required to provide proof of employment eligibility.

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!

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.