Senior ML Engineer (Agentic Search)

SpainRemoteFull-timesenior

Machine Learning EngineerData

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Requirements Summary

The ideal candidate brings strong applied machine learning or software engineering experience, a solid understanding of modern ML techniques, and practical expertise deploying models in production.

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Machine Learning EngineerData

Join an innovative engineering team building an agent-native search platform that enables AI systems to access fresh, relevant, and reliable information in real time. As a Senior ML Engineer, you will design, train, and deploy machine learning models powering retrieval, ranking, and indexing at scale. You will help solve complex challenges in search relevance, large-scale data processing, and production ML systems. Working across machine learning and software engineering, you will contribute to a high-availability platform supporting demanding, high-throughput workloads. You will collaborate with engineering teams to translate advanced ML techniques into reliable production services. This role offers substantial technical ownership, opportunities to shape architecture, and the chance to influence the future of AI-powered information access.

You will lead the development and optimization of machine learning capabilities for large-scale, agent-oriented search systems. Your work will focus on improving search quality, retrieval performance, and operational efficiency while collaborating with engineering teams to bring advanced models into production.

Design, train, deploy, and maintain machine learning models for retrieval, reranking, and search relevance in production environments.
Build and optimize embedding-based indexing and large-scale information retrieval systems.
Develop machine learning models supporting web crawling, data selection, and content understanding.
Define meaningful quality metrics for agent-native search and build robust model evaluation pipelines.
Work on large-scale systems handling high-throughput query workloads and continuous production traffic.
Collaborate with engineering teams to integrate ML models into production services and infrastructure.
Analyze trade-offs between latency, search quality, scalability, and operational cost.
Research, experiment with, and apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems.
Contribute to product strategy, technical architecture, and engineering decisions.
Continuously improve model performance, evaluation methods, and production reliability.

Requirements

~2 min read

The ideal candidate brings strong applied machine learning or software engineering experience, a solid understanding of modern ML techniques, and practical expertise deploying models in production. You should be comfortable tackling complex retrieval and ranking problems, working with large-scale data systems, and collaborating with distributed teams in a fast-moving environment.

At least 5 years of experience in software engineering or applied machine learning.
Strong programming skills in Python, Go, or C++.
Proven experience deploying and operating machine learning models in production systems.
Hands-on experience with retrieval, ranking, recommendation systems, or related machine learning applications.
Strong understanding of machine learning and modern deep learning techniques.
Experience working with large-scale data systems and high-throughput environments.
Ability to design evaluation frameworks and define meaningful model performance metrics.
Strong analytical, problem-solving, and critical-thinking skills.
A product-oriented mindset, with a focus on measurable impact, experimentation, and iterative improvement.
Ability to take ownership of technical challenges and collaborate effectively with distributed teams.

Experience with search engines or large-scale information retrieval systems.
Familiarity with embeddings, transformer architectures, and modern natural language processing techniques.
Experience developing LLM-powered applications or agent-based systems.
Contributions to open-source projects, technical publications, or conference presentations.
Participation in competitive machine learning initiatives, such as Kaggle, or other evidence of strong technical expertise.