Top Machine Learning Jobs in Barcelona

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

As a Machine Learning Senior Engineer, you will develop algorithms, enhance pipelines, analyze data, and mentor team members while solving unique modeling challenges.

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

Design, build, and deploy production-quality AI/ML solutions (NLP & Computer Vision). Own end-to-end ML lifecycle including data cleaning, model development, MLOps, automated deployment, monitoring, testing, and optimization in cloud environments.

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.

Build and operate ML infrastructure and platforms: design systems for training, evaluation, deployment, inference, and experimentation; optimize serving for low latency/high throughput; develop reproducible data pipelines, observability, benchmarking, and reusable platform primitives to enable rapid model iteration and reliable production AI workloads.

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

Design and build end-to-end ML systems for fraud detection: develop and deploy models, create production-ready features and real-time data pipelines, integrate with backend services, and ensure monitoring, security, and observability.

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.

Senior backend developer building production-grade microservices in Java and Python, integrating machine learning models (e.g., TensorFlow), working with cloud, Docker/Kubernetes, Git, and Agile/Scrum. Remote role with initial onboarding in The Hague; requires work permit and fluency in English.

Remote AI/ML Architect to assess current AI/ML landscape, document requirements, and propose SDLC, governance, and deployment approaches. Provide recommendations to architecture and ICT management. Onboarding in the Netherlands (first week). Candidates must be based in Europe with a valid work permit.

Design and document the current AI/ML landscape, capture requirements, propose SDLC approaches and tooling, and advise architecture and ICT management on AI/ML solution design, governance, and deployment. Remote role with first-week onboarding in the Netherlands.

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.

Design, train, fine-tune, distill, and deploy LLM-based and agentic ML models. Build and operate agent workflows in Python, run evaluation and benchmarking, select models and cost strategies, and manage production monitoring and observability for model performance and reliability.

Own, design, build, and operate distributed, high-load backend services and data pipelines for ML infrastructure. Partner with ML and product teams to deliver self-service, reliable model serving, storage, and deployment components; ensure observability, scalability, and performance while driving technical decisions and continuous improvement.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Build and ship state-of-the-art speech systems end-to-end: model architecture, training (pre-train/fine-tune/post-train), evaluation, tooling, data curation, production inference, and safety/abuse mitigation for voice conversion and related tasks.

Build and operate production-grade model serving infrastructure, design deployment pipelines (blue/green, canary), implement autoscaling and multi-model serving, optimize GPU utilization and network throughput, set up observability and model registries, manage CI/CD for reproducible deployments, own full ML system lifecycle including on-call support and platform scalability.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Design, train, and improve computer-vision and generative ML models; handle end-to-end ML pipeline from data collection to deployment; prototype research ideas and convert successful prototypes into product-ready solutions; optimize models for efficient on-device inference and collaborate with product teams to integrate models.

The AI Director will oversee ML bot development and implementation, identify AI opportunities, educate teams, and prototype AI solutions.