Top Machine Learning Jobs in Spain

Build and ship LLM- and agent-based prototypes and production solutions; develop ML and analytical models when appropriate; design reusable, scalable AI products; collaborate with business, platform, frontend, and delivery teams; join client discussions and move projects from experiment to delivery with high quality.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

As a Senior Product Manager, you will define product vision, analyze market trends, manage product KPIs, and collaborate with teams to deliver innovative solutions.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

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

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

Lead and grow a ~50-person ML organization, prioritize high-impact AI projects, set ML technical direction and standards, ensure delivery and measurable business value, and collaborate across products to translate AI research into product innovations.

As a software engineer in the research team, you will develop and maintain the core codebase, optimize workflows for researchers, and ensure production readiness while implementing best practices in ML software development.

As an AI/ML Engineer at Air Apps, you'll design and optimize machine learning models for mobile applications, collaborate with teams, and innovate enhancements through AI-driven solutions.

The AI / Machine Learning Researcher will conduct applied research, develop prototypes, and work with data to create AI-driven features related to home design.

Conduct applied AI/ML research to solve home design challenges, work with raw data, test hypotheses, and build prototypes/MVP solutions.

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.

Build and deploy production-grade generative AI and agentic systems for enterprise customers. Responsibilities include developing RAG applications, backend services, data pipelines, evaluation harnesses, monitoring, guardrails, and cloud-native AWS infrastructure. The role owns model and agent reliability, cost, latency, drift detection, and release quality. It also involves architectural decisions, customer handover, documentation, mentoring engineers, and collaboration with forward-deployed executives and engineers.

Lead pre-sales technical discovery and serve as the primary architectural authority for enterprise AWS AI/ML, data, and cloud modernization initiatives. Design Bedrock and SageMaker solutions, migration strategies, and modern data platforms; develop AWS cost models, SOWs, proposals, and funding inputs; evaluate risks and compliance requirements; and coordinate technical handoffs with delivery teams. The role partners with account executives, AWS field teams, customers, and internal engineers without carrying a sales quota.

Design, build, evaluate, and deploy production-grade LLM and classical ML solutions. Create retrieval and agentic workflows, integrate AI into client apps, implement evaluation frameworks, optimize prompts and pipelines, and collaborate with product teams through discovery to production support.

Owns the end-to-end lifecycle of machine learning and LLM solutions, including development, fine-tuning, evaluation, deployment, monitoring, retraining, inference optimization, and scalable MLOps pipelines. The role involves improving experimentation, versioning, A/B testing, and ML infrastructure; collaborating with data, engineering, and product teams; contributing to architecture; and mentoring junior ML engineers.

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

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.

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.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.

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.

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.

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.