Top Machine Learning Jobs in Spain

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.

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.

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, deploy, and monitor machine learning models for games; improve Python and SQL pipelines; build Streamlit interfaces; work with LLMs, RAG, and agentic AI prototypes; collaborate cross-functionally and communicate results to stakeholders.

Lead applied ML efforts to embed LLMs and agentic workflows into production, focusing on anti-fraud, automated evaluation, and operational automation. Design reusable architectures, implement observability and A/B tests, partner with delivery and clients, and set engineering standards for prompts, agents, pipelines, and CI/CD.

Design, prototype, and deploy ML models for personalization in mobile games. Implement and evaluate algorithms (including RL and generative methods), run A/B tests, convert prototypes to production via MLOps, monitor player data, and collaborate with product, engineering, and analytics teams to improve retention and monetization.

Build, prototype, and productionize ML models for personalization in mobile games. Design experiments and A/B tests, monitor player data, and translate research into deployable code while collaborating with cross-functional teams.

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.

As an AI/ML Engineer, you will develop and optimize machine learning models for mobile applications, collaborate with teams to enhance user experiences, and implement AI techniques to improve app performance.

Design, develop, deploy, and maintain scalable AI/ML systems and pipelines. Apply MLOps/LLMOps/AgentOps best practices, run large-scale experiments, build engineering infrastructure for batch and real-time workflows, and integrate models into business applications with monitoring and APIs. Collaborate with scientists, engineers, and product teams to deliver end-to-end solutions using Python, MLflow, Azure, and Databricks.

Design, implement, and productionize marketing-effectiveness ML and statistical models. Build scalable analytical pipelines using cloud, containers, and distributed systems. Optimize performance, ensure reproducibility, and collaborate with product, modeling, and frontend teams.

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.

Lead design and productionization of ML pipelines, evaluation systems, and AI agents for cybersecurity. Own experimentation-to-production lifecycles, integrate ML across product teams, scale ML infrastructure, optimize agent performance, and mentor junior engineers while collaborating with CTO and product leadership.

As a Senior AI/ML Engineer, you will develop and deploy AI initiatives, focusing on ML solutions and ensuring robust software integration. Responsibilities include coding, MLOps implementation, Agile delivery, and collaboration with stakeholders to translate business needs into technical solutions.

As an AI Machine Learning Engineer, you'll develop and deploy AI solutions, implement MLOps workflows, and partner with stakeholders to translate business challenges into technical requirements.

Design, build, and maintain scalable AI/ML infrastructure and production ML pipelines. Serve and monitor models at scale, implement CI/CD and GitOps, manage containerized deployments, collaborate with data scientists and engineers, and drive model observability, drift detection, and continuous improvement.

Design, develop, and deploy ML-powered data products; own full ML model lifecycle from framing and experimentation to production deployment, monitoring, and iteration; collaborate with Data Analysts and Data Engineers to ensure robust pipelines, feature availability, and measurable business impact.

Build production-grade machine learning systems that connect data pipelines to user-facing software. Design robust system architecture, implement model inference, ship resilient full-stack/cloud services, and collaborate on UX-driven solutions using Python, APIs, and AWS.

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.

As a Senior Machine Learning Engineer, you will develop and scale customer-facing AI models, ensuring quality integration with business solutions, and mentoring peers.

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.

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

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.