Top Machine Learning Jobs in Barcelona

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

Design, build, and operate ML platform systems to train, evaluate, deploy, and serve generative models. Improve reliability, scalability, observability, and cost-efficiency of GPU and cloud workloads. Build internal tools and agent-friendly workflows, collaborate with researchers and engineers, and drive platform architecture and automation.

Design, build and scale an automated translation-quality evaluation framework for localisation and dubbing. Implement production ML evaluation infrastructure, roll it out across teams, align metrics with product signals, and maintain/iterate systems in collaboration with R&D and cross-functional partners.

The role involves creating and improving ML models, collaborating with teams, developing experimentation roadmaps, and ensuring optimal performance of production models.

The Senior ML Engineer will create and improve ML models, collaborate with teams, monitor models in production, and maintain documentation.

The Senior FinCrime Product Owner will design and implement a financial crime prevention product, develop strategy, and lead product delivery at Deblock.

As a Machine Learning Engineer, you will build and scale ML infrastructure, manage deployments, and optimize models for production, collaborating with researchers and engineers.

The Machine Learning Engineer will design, build, and maintain scalable ML systems, develop end-to-end ML pipelines, and implement MLOps practices while collaborating with product and engineering teams to enhance AI products.

Design, build, and deploy AI/ML features for conversation intelligence: develop end-to-end pipelines, implement model development and production inference, instrument services with metrics and telemetry, participate in on-call rotations and progressive rollouts, and collaborate across product and engineering to maintain model performance and reliability.

You will analyze model behavior in a live auction environment, propose enhancements, and collaborate on performance monitoring tooling.

Own and evolve Preply's ML platform: design experiment tracking, artifact management, GPU training and scalable inference, CI/CD for ML, observability, and LLM/RAG capabilities while mentoring engineers and aligning platform delivery with product goals.

The Senior Machine Learning Engineer will build scalable AI tools, collaborate with cross-functional teams, and optimize AI pipelines using cloud resources. The role involves mentorship and development of ML infrastructure.

Manage the end-to-end product lifecycle for financial crime tools, focusing on compliance, innovation, and backlog management while liaising between engineering and business teams.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

Lead the Applied AI Team in developing and deploying ML systems for clinical care, mentoring engineers, and driving initiatives across the AI stack.

As an ML Researcher, you will design evaluation frameworks, manage evaluation infrastructure, and track model performance through internal and external benchmarks.

Lead ML infrastructure upgrades in hyperscale data centres across EMEA: manage contractors, schedules, logistics, mechanical/electrical/fibre coordination, commissioning (L3/L4 support), risk mitigation, reporting, and lessons learned throughout full project lifecycle.

Lead applied research on scalable AI/ML solutions with emphasis on Small Language Models and GenAI. Design and run experiments, develop model architectures, evaluate performance, and translate research into production-ready, efficient, and usable systems while collaborating with cross-functional stakeholders.

Lead the development and deployment of advanced AI and machine learning models, mentor junior engineers, and ensure scalability and robustness of AI solutions.

The Senior Machine Learning Platform/Ops Engineer will design and productionize ML systems with a focus on reliability, performance, and observability, collaborating with ML Scientists and Backend Engineers.

The Machine Learning Intern will learn advanced technologies in ad-tech, develop algorithms, manage datasets, and collaborate with a motivated team.

As a Machine Learning Engineer, you'll apply state-of-the-art techniques to projects, enhance customer experiences, and communicate results effectively.

Build ML prototypes and data pipelines, collaborate with researchers and product engineering to integrate models into Fusion, containerize and deploy applications, implement best practices, and document architectures to accelerate research-to-production workflows.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

Design, build, and deploy AI-powered features for a digital asset management platform using AWS AI services and LLM APIs. Implement embedding-based search, agentic AI workflows, prototypes, and collaborate with product and engineering teams to transition prototypes into production.