Top Machine Learning Jobs in Barcelona

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.

Build and maintain scalable backend systems that integrate real-time data, machine learning, and optimization models to optimize renewable and battery storage operations. Own architecture through deployment and maintenance, collaborate with engineers and data scientists, troubleshoot performance issues, and improve monitoring, reliability, and system performance.

Design, build, deploy, and continuously improve production-grade machine learning systems for a proactive AI platform. Own projects end to end, including data curation, model training, evaluation, inference, monitoring, troubleshooting, and optimization. Translate research into reliable systems while balancing latency, cost, safety, and scalability. Collaborate with research, engineering, and product teams, mentor engineers, and drive complex initiatives independently.

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.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

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

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

Develop, deploy, and maintain scalable AI/ML pipelines and production models, including LLMs and generative AI. Optimize model performance, latency, and cost across AWS, GCP, and Azure. Apply MLOps practices such as CI/CD, versioning, monitoring, logging, and maintenance. Collaborate with data scientists, software engineers, solution architects, and cross-functional teams to integrate AI capabilities into products and workflows.

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.

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.

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

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.

Design, prototype, and productionize ML models for pricing, carrier forecasting, and recommender systems. Lead applied research and product discovery, integrate LLMs and AI agents, collaborate with Product and MLOps to deploy and monitor models, and translate operational problems into measurable ML solutions that drive margin and efficiency.

Build and deploy production-ready ML models for pricing, carrier forecasting, and recommender systems. Conduct deep data science and statistical exploration, partner with product and operations to translate problems into ML solutions, integrate LLMs and AI agents, and collaborate with the MLOps platform to productionize and monitor end-to-end projects.

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

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.

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.

Design, prototype, and productionize ML models for pricing, carrier forecasting, and recommender systems. Conduct deep statistical analysis, partner with product and operations for discovery, integrate LLMs where appropriate, and collaborate with MLOps to deploy and monitor end-to-end solutions.

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.

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, 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.