Top Artificial Intelligence Jobs in Madrid

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.

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.

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

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.

Build and own end-to-end data foundations, including ingestion pipelines, dbt models, data marts, semantic layers, and AI-ready datasets. Integrate fragmented marketing, advertising, ecommerce, analytics, and CRM data across a multi-tenant Snowflake environment. Manage platform API changes, data quality, restatements, performance, and client-specific modeling. Collaborate with product, engineering, AI, BI, and client teams while using AI coding agents to accelerate reliable production development.

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

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.

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.

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.

Design, build, and deploy production-grade agentic AI systems: multi-agent orchestration, RAG pipelines, provider integration, LLMOps, observability, and reusable patterns. Embed with client teams, lead design sessions, tune embeddings/vector search, manage cost/latency tradeoffs, and define metrics for accuracy, safety, latency, and cost.

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.

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.

As a Senior AI/ML Engineer, you'll design and develop AI solutions, build production-level ML models, and implement MLOps practices. You'll work with a multidisciplinary team on cutting-edge projects, focusing on AI-powered software and deep learning techniques.

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.

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

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.

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.

Develop and enhance the Ops AI Platform, focusing on building AI agents for operational tasks, integrating them with internal tools, and ensuring user autonomy in operations. Mentor engineers and influence system architecture.

As a Full-stack Software Engineer, you will enhance developer workflows, build AI-native tools, and ensure robust infrastructure for product teams at Alan, focusing on seamless integration and operational excellence.

The AI Engineering Intern will design, test, and deploy scalable AI systems, integrate models, and work on inference optimization, collaborating with data scientists on complex problems.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

AI Engineers at QuantCo design, build, and implement applied AI systems, working with data scientists to optimize inference, develop scalable solutions, and integrate AI into enterprise operations.