Top Machine Learning Jobs in Spain

Own an AI neobank product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, reconciliation, security, scalability, and failure handling. Incorporate regulatory requirements across KYC, monitoring, limits, privacy, and licensing. Apply AI with appropriate evaluation, safeguards, oversight, and reliability controls while aligning engineering, design, compliance, operations, treasury, and finance.

Collaborate with clients and consulting teams to solve complex business challenges through quantitative analytics. Responsibilities include designing analytical solutions, developing prototypes, applying machine learning and NLP models, validating GenAI tools, modeling financial risk, optimizing promotions, and assessing solution effectiveness. The role involves communicating sophisticated data science concepts clearly, working across industries and international projects, and continuously expanding technical expertise.

Develop analytical and machine learning solutions for global consulting clients across financial services, energy, retail, transportation, and other industries. Responsibilities include designing solutions, building prototypes, evaluating effectiveness, developing NLP and predictive models, applying advanced analytics, and communicating data science concepts to clients and colleagues. The role offers coaching, international exposure, and hybrid collaboration.

Build and operate data and AI governance systems, including data quality testing, catalogs, metadata, lineage, ownership frameworks, model inventories, and risk workflows. Partner with data, engineering, legal, privacy, security, and business teams to support GDPR, SOC 2, and EU AI Act readiness. Enable self-service analytics and responsible AI through documentation, access governance, audits, guardrails, and automated quality monitoring.

Develop, validate, deploy, and maintain machine learning features that automate orthodontic treatment planning. Responsibilities include decomposing business needs into ML problems, building deep learning models for 3D meshes, text, and tabular data, analyzing model performance, collaborating on production architecture and integration, researching scientific advances, and transferring technology across product teams.

Lead end-to-end architecture, implementation, and delivery of Blue Yonder Warehouse Management and related execution solutions. Serve as the primary client contact, manage timelines, budgets, risks, and deliverables, and lead solution teams and integrations. Provide functional and technical guidance, mentor junior staff, align solutions with business objectives, and collaborate with project managers, delivery partners, and stakeholders to deliver successful client outcomes.

Build and extend the platform layer: design shared backend services and APIs, improve reliability and correctness, collaborate with product teams to remove platform bottlenecks, mentor engineers, shape architecture and migrations, and write high-quality fullstack code with a backend focus.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Lead a globally distributed endpoint security engineering team developing visibility, prevention, machine learning, and cross-platform protection capabilities. Own the roadmap, delivery, hiring, mentorship, and technical direction across Windows, macOS, and Linux. Guide malware analysis and reverse-engineering work, coordinate with product and engineering teams, maintain vendor relationships, and represent Elastic through customer engagement, research publications, and conference presentations.

Lead global accounting and controlling across European entities, establishing accounting policies, controls, IFRS compliance, audit processes, and treasury operations. Transform distributed finance workflows into centralized, scalable functions; optimize systems, automation, and data architecture; and build a high-performing international team. Partner with the CFO and FP&A on reporting, cash-flow forecasting, business performance, M&A integration, and strategic decision-making while engaging auditors, regulators, boards, and operational leaders.

Design, build, and operate customer-facing and internal data products, pipelines, and analytics solutions using Databricks, Spark, Python, and SQL. Develop frameworks for acquiring, processing, monitoring, and extracting value from large datasets. Contribute to data-platform architecture, data quality, observability, automation, testing, security, and CI/CD while collaborating with product owners and cross-functional stakeholders.

Own a from-scratch rebuild of Nory's mobile app using React Native and TypeScript, ensuring reliability on low-spec devices and poor connectivity. Set architecture, design system, and migration plan, ship an MVP, integrate AI agents to speed development, and grow the codebase into a platform for cross-functional teams while maintaining high UX and reliability standards.

Design, build, and own scalable backend services, APIs, and high-performance web frontends for an autonomous procurement platform. Improve reliability, security, and performance, implement async task pipelines, and collaborate with product and design. Mentor juniors, participate in code reviews, and leverage AI-assisted tools while shipping iteratively in a globally distributed, async-first team.

Own the end-to-end Juno AI shopping assistant experience, including conversational UX, recommendation logic, user testing, roadmap, rollout strategy, quality metrics, and competitive positioning. Collaborate with design, engineering, and the Catalog team to define and launch a new AI-powered product from the ground up.

Lead the design, implementation, support, and optimization of Microsoft 365 and digital workplace solutions for global users. Provide technical consultancy, manage critical incidents, ensure security and reliability, integrate AI capabilities, analyze workplace data, document processes, and mentor junior engineers. Collaborate with stakeholders and IT teams to translate business requirements into scalable, highly available technical solutions.

Serve as a pre-sales technical advisor helping enterprise customers integrate and deploy Claude. Translate business needs into scalable AI architectures, support API and workplace implementations, develop evaluation frameworks, deliver technical content, coordinate cross-functional teams, and share integration insights with Product and Engineering. The role requires customer-facing architecture expertise, strong communication, Python proficiency, LLM familiarity, and occasional customer-site travel.

Develop and deploy data science solutions for marketplace optimization, including dynamic pricing, fleet repositioning, demand and supply balancing, and experimentation. Build production machine learning models, MLOps workflows, scalable data pipelines, monitoring dashboards, and KPIs. Analyze algorithm performance across international markets and communicate complex insights to non-technical stakeholders.

Operationalize and scale machine learning, generative AI, and agentic solutions for biologics discovery. Build production pipelines, deployment and serving capabilities, lifecycle management, monitoring, observability, versioning, governance, CI/CD, and scalable compute workflows. Partner with data scientists, AI/ML practitioners, technology teams, and scientific domain experts to transition models from experimentation to reliable production use.

Design and implement enterprise AI and Agentic AI architectures that are scalable, secure, reliable, governable, and maintainable. Translate business and technical requirements into architectures spanning AI agents, foundation models, data platforms, APIs, orchestration, observability, security, and cloud infrastructure. Integrate AI solutions with enterprise systems, guide implementation and architecture reviews, evaluate emerging technologies, promote responsible and sustainable AI, and help transition experiments into production-grade solutions.

Own the technical roadmap for Veriff’s physical document identity verification product, balancing accuracy, coverage, fraud detection, conversion, latency, and cost. Partner with ML engineers, data scientists, operations, annotation teams, analytics, legal, marketing, and go-to-market teams. Lead technical customer discussions, define model success metrics, investigate data issues, manage trade-offs, and launch scalable product improvements.

Lead the strategy, development, deployment, and scaling of AI-powered health products across governments and public health systems in the Global South. Own product roadmaps, execution, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, data science, government, donor, and implementation teams to embed sustainable, measurable AI solutions across Africa and Southeast Asia.

Develop machine learning algorithms and manage cloud-based datasets for a mobile advertising demand-side platform. The intern will work with Python, SQL, data warehousing, deep learning tools, AWS services, and reporting or visualization platforms while collaborating with a technology-driven team.

Lead architecture and hands-on implementation of scalable decision automation and production ML systems for Candy Crush Saga. Build reusable decisioning foundations, improve experimentation and causal inference tooling, mentor senior engineers, and ensure reliability, observability, and cross-team alignment for player-facing personalization and adaptation at scale.

Lead the strategy, design, deployment, and scale-up of AI-powered health products across the Global South. Own product roadmaps, execution, government-system integration, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, machine learning, program, and domain teams to deliver sustainable, measurable public-health impact.

Design, train, and evaluate large multimodal and vision-language-action models for robotic agents. Develop reinforcement and imitation learning methods, embodied datasets, simulation environments, world models, planning systems, and sim-to-real experiments. Build robust research software and distributed training infrastructure, validate approaches on physical robots, and collaborate with research and engineering teams to deploy reliable systems. Communicate findings through technical reports, open-source releases, demonstrations, and publications.