Top Machine Learning Jobs in Spain

Lead and strengthen trade processing controls across the trade lifecycle, resolve complex operational issues, drive process improvements and automation (including AI/ML use cases), partner with Technology and Front Office, maintain risk and control frameworks, support global standardisation and represent TPG in cross-functional initiatives.

Lead design and adoption of the Operations Target Operating Model, govern and deliver Ops improvement initiatives (including automation and AI), quantify and track cost-to-serve impact, coordinate multi-stakeholder M&A and growth activities, and produce COO-ready strategic outputs. Own KPI frameworks, initiative prioritization, and enablement of automation/AI use cases while coaching associates and ensuring TOM adoption across Operations.

Lead and strengthen trade processing controls across the trade lifecycle, resolve complex trade exceptions, drive operational improvements and automation/AI use cases, partner with Front Office, Technology and Risk, and support global standardisation and audit-ready controls.

Lead and strengthen data quality controls for reference and market data across asset classes. Investigate complex data issues, drive root-cause remediation, partner with global stakeholders and vendors, and identify automation and AI use cases to improve controls, governance, and operational efficiency.

Design, develop, and maintain local instances of Santander's AI Knowledge Base: knowledge representation, graph schemas, entity resolution, semantic indexing, and retrieval pipelines. Work hands-on with data, engineering, and product teams to deploy scalable, reliable knowledge-driven systems for conversational agents and autonomous tooling, aligning local solutions with global standards and contributing to evaluation, monitoring, and reusable components.

Lead strategy and roadmap for BidMachines supply-side product. Own product lifecycle, collaborate with engineering, sales, publishers and data teams, analyze supply performance, and drive features for inventory management, auction dynamics, and demand-supply matching to maximize publisher revenue and advertiser satisfaction.

As an AI Director, lead AI and data programs for Specialty Chemicals, drive account growth, mentor teams, and engage C-level stakeholders strategically.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

The Senior AI & Computer Vision Software Engineer will work on innovative software solutions, bridge research and production, and mentor others while working in an Agile environment.

Lead Westernacher's Spain market by defining and executing market strategy, driving sales and profitability, and building client relationships. Split role between ~50% commercial leadership (business development, go-to-market, partner management) and ~50% delivery engagement (Project Director/Program Manager ensuring successful ERP/SAP transformations). Recruit and develop local team and ensure delivery and quality standards.

Design, build, and maintain scalable cloud data pipelines, databases, and analytics platforms using Azure and Databricks. Integrate industrial/time-series data, develop ETL/ELT and API-based integrations, define data models and governance, and support analytics, AI-driven solutions, predictive maintenance, and operational reporting in collaboration with cross-functional teams.

Design, build and deploy production-grade agentic AI systems and LLM/RAG features. Own multi-agent orchestration, prompt engineering, evaluation, and full ML lifecycle from training pipelines to online inference and monitoring. Improve ML platform and tooling (MLflow, Airflow, SageMaker, Kubernetes), collaborate cross-functionally, and lead research on advanced RAG, graph representations, and agentic evaluation.

Lead an international engineering team to design, implement, and deploy ML/Generative AI/vision systems (including LLMs, RAG, agentic AI) into cloud production. Manage project scope, risks, timelines, stakeholder communication, and technical direction while ensuring quality, documentation, and cross-team collaboration.

Design, build, and operate scalable search, retrieval, and personalization systems. Develop ML models across candidate generation to ranking, leverage embeddings and LLMs, run A/B experiments, optimize for latency and scalability, and collaborate with product and data teams to deploy robust production ML services.

As a Senior Data Scientist at Back Market, you will design and implement machine learning models to optimize pricing strategies, collaborate with engineers, and communicate findings to stakeholders.

Lead and scale a Fraud Operations team to protect marketplace financial integrity. Own roadmap, KPIs, investigation standards, chargeback management, governance, workforce planning, and cross-functional partnership with Product, Data, Payments, and Finance to improve detection and reduce financial exposure.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

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.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.