Top Artificial Intelligence Jobs in Barcelona

Lead development and scaling of AI-powered clinical applications using LLMs. Gather user and industry requirements, write product specifications, prioritize roadmaps, coordinate sprints and releases, validate quality with engineering and QA, and design generative AI capabilities to streamline pharmaceutical workflows and regulatory documentation.

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.

Lead Lesson Insights product end-to-end: strategy, roadmap, and delivery of AI/LLM-driven feedback across web and mobile. Prioritize actionable feedback, connect per-lesson insights to long-term progress, involve tutors, scale to languages, drive engagement and reach, and perform hands-on data analysis and experimentation to measure impact.

Build end-to-end AI-native product features across frontend, backend, and LLM integrations. Design and deploy agent workflows, streaming real-time interactions, memory, and tool integrations. Improve reliability, observability, fallbacks, and reduce latency while collaborating with ML and product teams to ship production-grade, multi-step workflow capabilities.

Build and ship end-to-end AI features: design prompts, tools, memory, and agent workflows; turn model outputs into reliable behavior; debug across model, orchestration, infra, and UX; optimize latency, cost, and reliability; develop evaluation frameworks and collaborate with product and engineering to deliver robust ML-powered features.

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.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

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 innovate model compression techniques for multimodal AI systems, focusing on quantization, knowledge distillation, and pruning for efficient deployment on edge devices.

As an AI Research Engineer, you will develop multi-modal reinforcement learning systems, focusing on optimization across data modalities, conducting research on algorithms, and building infrastructure for scalable training and evaluation.

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 AI Developer will design, implement, and deploy AI agents and workflows, integrating advanced models and ensuring reliable performance in production environments.

Own product capabilities that improve brand visibility across search engines and LLMs. Evaluate visibility signals (backlinks, mentions, PR, communities), design agentic AI workflows, advise on off-page and technical SEO, analyze review ecosystems and media channels, prototype AI-agent solutions, and drive customer adoption with cross-functional teams.

Lead end-to-end architecture for enterprise data, analytics, and AI in a Microsoft-first environment. Translate business needs into secure, scalable designs, prototype and guide delivery teams, ensure governance, security, and production readiness, and enable enterprise AI (Azure OpenAI, RAG) and BI (Power BI) solutions.

Lead operations of large-scale NVIDIA GPU and Kubernetes-based AI infrastructure. Drive incident response, troubleshooting across Linux, networking, storage, and hardware. Improve reliability, observability, automation, and platform readiness; mentor engineers and define operational standards and runbooks.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

Lead and build Maze’s new AI-native Market Research service: define research vision and methodologies, recruit and manage a research team, own delivery quality, partner with Product/Engineering and GTM, and serve as executive research lead on strategic customer engagements.

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.

The Director of Business Development will drive revenue through creating strategies for new business, managing client relationships, and overseeing sales processes while ensuring high service standards.