Top Artificial Intelligence Jobs in Madrid

The role involves architecting AI solutions for compliance, ensuring regulatory adherence, automating processes, and enhancing team capabilities through technology and training.

Design and implement user-facing features from concept through implementation, combining UX design and front-end engineering. Build, iterate, and maintain UI/UX and business logic to drive engagement and conversion, collaborate with product/marketing/support, participate in code reviews, and leverage modern tools and deployment workflows.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.

The role involves providing legal guidance on global privacy and AI issues, collaborating with teams to ensure compliance, and developing scalable processes.

Lead full‑stack development of high‑traffic public websites, integrate AI tooling into the SDLC, design scalable backend and performant UIs, maintain cloud infrastructure, CI/CD, monitoring, and testing, and own services under a you‑build‑it‑you‑run‑it philosophy.

The AI Applied Scientist Intern will tackle complex problems using deep learning, statistical analysis, and large datasets, collaborating with a team to implement AI solutions that generate economic impact.

Own lifecycle marketing campaigns across email, push and in-app channels: write copy, build automated customer journeys and behavioral triggers, run A/B tests, analyze performance with product analytics, and work with Product and Tech to optimize activation, retention and monetization.

Lead integration of LLMs and generative AI into production applications, implement RAG and prompt-engineering solutions, build CI/CD pipelines, collaborate with cross-functional teams, mentor engineers, and drive AI automation and platform improvements.

Partner with government customers and partners to design, prototype, and deploy AI solutions (computer vision, LLMs, generative/agentic AI). Create technical collateral, lead hands-on trials and deployments using NVIDIA hardware/software, and advocate product requirements across product, sales, and engineering teams.

Lead product direction and execution for major areas of an AI Neobank app. Define roadmaps, priorities, standards and metrics across squads; guide PMs, designers and engineers; translate regulatory and operational constraints into product decisions; drive launch quality, post-launch learning and continuous improvement across payments, savings, investing, insurance and travel features.

Own product areas in BJAK's AI neobank app: define problems, scope, metrics, roadmaps; work with engineering, design, data, compliance; launch features, monitor performance, iterate, and balance speed, UX, business impact, and operational complexity.

Operate within the CEO Office to turn CEO priorities into execution across product, hiring, licensing, partnerships and market launch. Track OKRs, manage decisions, blockers and owners; prepare briefs and follow-ups; coordinate cross-functional teams; identify and close execution gaps; own urgent cross-departmental business problems.

Build and scale Jimdo Companion: design agent orchestration, LLM-powered workflows, recommendation engines, APIs and full-stack features; enable AI platform capabilities, collaborate cross-functionally, and drive product-quality AI in production.

Design, implement, and evaluate applied research projects to improve alignment, safety, and evaluation of LLM-based guardrails. Work on experiments (e.g., synthetic data quality metrics, explanations for blocked content), deliver results with mentor guidance, and present findings to the team to shape product capabilities.

Partner with Product, Design, Engineering, and Data teams to define KPIs and measurement for AI-powered features, guide instrumentation, design and evaluate experiments, build dbt models, create dashboards, and translate analysis into product recommendations and analytics best practices.

Build and ship agent orchestration workflows, integrations, and shared infrastructure for AI-driven growth across paid media, lifecycle, organic content, CRO, incentives, and referrals. Implement monitoring, audit logging, creative generation pipelines, and reliable integrations so domain experts can tune and operate workflows independently.

Design, build, orchestrate, and deploy production-ready AI agents and LLM-based solutions. Partner with domain experts to identify high-value use cases, redesign workflows using AI-first principles, integrate AI workflows into business infrastructure, and support internal AI enablement and training.

Build and ship native Android features in Kotlin for an AI-enabled neobank app. Design clean user flows, integrate backend APIs, improve performance and reliability, use analytics to iterate, and add on-device AI where it improves UX.

Design, build and ship Android features using Kotlin and Jetpack (Compose). Integrate backend REST APIs, improve performance, stability and memory, and craft clean user journeys for financial services. Use analytics and production debugging to iterate and deliver AI-assisted mobile experiences where beneficial.

Design, build, and run production-grade agentic AI systems for enterprise clients: architect multi-LLM integrations, RAG pipelines, orchestration, LLMOps, and cloud-native deployments. Lead client engagements, set engineering standards, measure system metrics, and share reusable patterns to scale agentic solutions across verticals.

The role involves delivering features across the CRM platform for distributors, using AI tools, handling fullstack development tasks, and collaborating closely with teams and customers.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

Design, build, and optimize high-performance AI infrastructure systems to support intelligent agents, ensuring reliability and scalability for millions of users.

Lead development and evaluation of speech language models and automated scoring systems for learner and hiring applications. Build, fine-tune, and monitor acoustic, ASR, speech-to-speech, and LLM-based models; design experiments and validity analyses; perform error analysis and rater reliability studies; integrate with product and UX; apply responsible AI practices and support production monitoring and governance for high-stakes assessments.

Lead R&D and implementation of ML and statistical learning capabilities for English language learning products. Design, build, and monitor production-ready models (NLP, speech, psychometrics), document and present results, mentor junior colleagues, collaborate with engineering, and publish research.