Top Machine Learning Jobs in Spain

Design, train and deploy scalable ML models and end-to-end pipelines (feature engineering, scoring, deployment, monitoring). Lead AI/ML proofs of concept, make architecture decisions, ensure code quality and CI/CD, monitor production models for drift, and mentor junior engineers while partnering with product and business teams to deliver production-ready solutions.

Lead technical qualification, design, and delivery of AWS-based data, analytics, ML and AI solutions. Translate requirements into scalable AWS architectures, drive complex projects, contribute to SOWs and handovers, identify risks and opportunities, and mentor colleagues while building repeatable services and accelerators.

Build and maintain scalable MLOps infrastructure and CI/CD for ML and generative AI. Implement experiment tracking, model versioning, deployment, monitoring, IaC, containerized workloads, and production lifecycle management. Collaborate with data scientists and engineers to productionize, monitor, retrain, and govern models in cloud-native environments.

Lead statistical strategy and delivery for Phase I-IV clinical trials embedded with a pharmaceutical sponsor. Design analyses, support protocol and reporting activities, oversee programming/CRO work, apply SAS/R (R Shiny)/Python and ML/DL methods for analyses and automation, and mentor junior statisticians.

Research and investigate global fraud trends using BioCatch data and external sources. Produce data-driven insights, visualizations, written thought leadership, dashboards, and presentations. Build repositories and automate analysis workflows while applying fraud expertise and AI tools to validate findings and support sales and marketing.

Lead end-to-end ML product roadmaps and initiatives (e.g., Attachment Matcher, Transaction Categorizer). Collaborate with data scientists, engineers, and stakeholders to define, deliver, and monitor ML solutions that drive measurable business value for SMEs across Qonto's platform.

Lead the Weaviate Database product: own roadmap and strategy, translate customer and research insights into features across schema, storage, replication, and search quality, coordinate cross-functional teams, drive developer adoption, and measure feature outcomes post-release.

Develop and implement 3D geometric ML and classical geometry algorithms to analyze and generate 3D intraoral scans and teeth. Build data pipelines, train/adapt 3D models, optimize test-time performance, validate robustness, and collaborate with clinicians and product teams to ship applied research into products.

Design, develop, and deploy full‑stack ML solutions for real‑time bidding to predict user response and auction prices. Own end‑to‑end modeling, evaluation, A/B testing, and productionization at web scale while improving advertiser ROI.

Seeking Quantitative Finance Experts to train and evaluate AI models by assessing accuracy, reviewing responses, and providing domain-specific feedback. Requires expertise in Quantitative Finance and strong analytical skills.

The role involves developing and optimizing electrochemical measurement methods, mathematical modeling, algorithm development, and leading system validation for diagnostic technologies.

Lead Hopper's finance analytics, data warehouse, BI and AI enablement strategy. Own data pipelines, GCP cost optimization, finance AI automation, reporting COE, data governance, and audit controls. Build self-service analytics, establish model foundations and manage a cross-functional team partnering with FP&A, accounting, and engineering.

The role focuses on developing scalable personalization platforms using backend engineering, machine learning, and cloud technologies to enhance user experiences.

Build, iterate, and maintain a production ML platform and pipelines. Support Data Scientists with tooling for deployment and monitoring, define MLOps roadmap and best practices, collaborate with Data Engineering and DevOps, and evaluate/integrate new ML frameworks and real-time inference tools.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

The role involves developing deep learning models, optimizing algorithms, implementing machine learning solutions, and analyzing data to derive insights for business advancement.

Build and operate ML platform systems for training, evaluation, deployment, and serving of generative models. Improve reliability, scalability, performance, observability, and automation for GPU and cloud workloads. Develop internal tooling and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and operational improvements.

Build, maintain, and optimize ETL pipelines and analytical data warehouse environments. Deploy, monitor, and scale machine learning models in production; automate model deployment and recurring data processes. Collaborate with Data Science, Engineering, BI, Risk, and IT teams; troubleshoot performance issues and maintain technical documentation.

Own and ship core ML subsystems for a proactive long-horizon AI product: data prep, training, evaluation, inference, and reliable production deployment. Debug model and system failures using production signals, optimize for latency/cost/reliability/safety, iterate rapidly, and mentor other ML engineers while collaborating cross-functionally to deliver measurable user impact.

Design, build, deploy, and optimize machine learning and AI solutions (including LLMs, RAG, multi-agent systems and agentic workflows). Integrate models into production, evaluate performance, test prompts and few-shot examples, collaborate with cross-functional teams, and participate in code reviews and debugging to ensure reliable, scalable AI systems.

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.

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.

The Senior ML/AI Engineer will collaborate with data scientists to implement optimized ML workflows, develop tools for prompt management, and monitor system performance while managing data pipelines and integrating AI APIs.

Contribute to designing, implementing, and evaluating ML models, ensuring performance across the entire lifecycle while facilitating rapid development and testing of new ideas.