Top Machine Learning Jobs in Barcelona

Lead and mentor a data science team developing machine learning systems for threat detection and agentic AI. Set ML technical strategy, standards, roadmaps, and deployment practices; guide projects from research through production; collaborate with security, engineering, and product teams; develop advanced malware and behavioral threat models; communicate insights; and contribute to research, publications, patents, and hands-on technical work.

Own the vision, strategy, roadmap, and full lifecycle of a new AI-native clinical research product. Lead cross-functional engineering, design, data science, and commercial teams through influence, validate customer needs, launch products, and drive adoption and commercial outcomes. Apply AI/ML to clinical development and operations while balancing usability, connectivity, regulatory compliance, and data standards. Engage executive stakeholders, enterprise customers, data providers, and standards bodies.

Own the vision, strategy, roadmap, and full lifecycle of a zero-to-one AI-native product for clinical development and operations. Lead cross-functional engineering, design, data science, and commercial teams without direct reports. Validate customer needs, launch and scale products, define success metrics, and drive adoption and commercial outcomes. Apply AI/ML responsibly within regulated environments while integrating clinical workflows, data standards, and Evinova modules. Engage executives, enterprise customers, partners, and standards bodies to achieve product-market fit.

Develop and maintain production-grade computer vision software for AI-enabled parcel management and automated logistics systems. Responsibilities include processing camera data for real-time decisions, integrating AI models and frameworks, designing scalable architectures, optimizing and deploying solutions, and contributing to technology roadmaps. The role also involves collaboration with product and engineering teams, technical leadership, mentoring junior engineers, and establishing software engineering best practices.

Drive strategy and execution for N26’s Customer Risk Lifecycle and anti-financial-crime initiatives. Responsibilities include defining segment strategy, monitoring KPIs and SLAs, optimizing workflows, evaluating business cases, managing vendors, coordinating cross-functional projects, and aligning Product, Technology, Data, Growth, and Finance stakeholders. The role requires data-driven analysis, SQL proficiency, process automation knowledge, project management skills, and comfort with compliance and regulatory topics.

Build and deploy production-grade AI tools using LLMs, agentic systems, RAG pipelines, vector databases, and automated evaluation frameworks. Develop flexible AI services and full-stack integrations with Python, TypeScript, and modern frontend frameworks. Take solutions from prototype through production, measure business impact, collaborate with stakeholders, and extend reusable architectures across use cases.

Leads a multinational BDR organization of approximately 18 representatives through team leaders. Owns BDR strategy, playbook development, performance management, metrics, sales technology adoption, process optimization, and cross-functional alignment with Sales, Marketing, and senior leadership. The role combines strategic planning with hands-on operational leadership across European and US markets, driving scalable prospecting, qualification, conversion, and team development.

Develops advanced generative AI, machine learning, optimization, and agentic AI solutions for life sciences. Translates technical outcomes into business value, engages senior stakeholders, supports consulting-style client initiatives, and collaborates across AI, product, strategy, and UX teams to deliver measurable pharmaceutical and clinical-trial impact.

Develop advanced GenAI, machine learning, optimization, and agentic AI solutions for life sciences and pharmaceutical use cases. Translate technical outcomes into business value, present initiatives to senior stakeholders, manage client engagements, and collaborate across AI, product, strategy, and UX teams to deliver measurable digital transformation.

Hunts and closes new business with top-tier mobile app companies. Responsibilities include outbound prospecting, building relationships with growth and user acquisition leaders, managing the full sales cycle, negotiating commercial terms, maintaining Salesforce pipeline data, developing territory strategies, and representing Adwake at industry events. The role requires strong knowledge of mobile performance marketing, user acquisition metrics, attribution platforms, and consultative selling.

Lead global adoption of QVAC and Tether AI by originating and closing strategic partnerships with enterprises, institutions, and governments. Define go-to-market strategy, identify priority markets and use cases, develop AI business cases, and collaborate with product and engineering teams on deployments. Build and manage the global AI Expansion team, establish commercial processes and KPIs, monitor partnerships, and represent Tether in executive, government, and industry engagements.

Analyze player behavior and business performance to guide user acquisition, retention, and monetization decisions. Build data models, pipelines, Power BI dashboards, experiments, predictive models, and behavioral segmentation solutions. Collaborate with game teams and senior stakeholders, communicate insights, maintain reliable analytics workflows in GitLab, and explore AI and generative AI applications for reporting and player insights.

Analyze fraud patterns and losses, optimize detection rules and machine learning controls, assess risks for products and market launches, and develop executive dashboards. Design and implement fraud prevention strategies, identify control gaps, define remediation plans, monitor performance, and collaborate with Product, Engineering, Data Science, Operations, and Compliance to deliver scalable fraud solutions.

Lead AstraZeneca’s AI strategy and roadmap for oncology clinical development. Drive high-impact AI projects from problem definition through validation and adoption, partnering with clinical, biometrics, regulatory, and study teams. Develop advanced models and data-centric methods, establish external collaborations, represent the company in scientific forums, publish research, shape best practices, and mentor scientists.

Build BLP’s core software across infrastructure, backend, machine learning, and product extension platforms. Responsibilities include operating secure and observable cloud infrastructure, developing performant distributed systems, applying AI to business problems, and enabling customer process automation. The role emphasizes formal reasoning, correctness by construction, maintainable systems, type systems, and functional programming. Work is available onsite in Zurich or remotely within Europe.

Leads AI research and engineering for oncology clinical development, applying advanced machine learning to trial design, dosing, endpoints, safety evaluation, and decision-making. Develops and validates interpretable models, builds AI strategy and roadmaps, partners with clinical, regulatory, biometrics, and study teams, and translates research into governed clinical applications. The role also requires scientific publication, external collaboration, technical leadership, mentoring, and development of reusable platforms and playbooks.

Lead AI research and engineering for oncology clinical development, applying advanced machine learning to trial design, dosing, endpoints, safety evaluation, and portfolio decisions. Develop and validate reliable, interpretable models; partner with clinical, regulatory, biometrics, and study teams; build external collaborations; publish research; and mentor peers. The role requires translating AI innovations into scientifically rigorous, regulatory-ready solutions within a global pharmaceutical environment.

Leads forecasting and commercial assessment for Sanofi’s pipeline and external pharmaceutical assets. Responsibilities include patient-based revenue modeling, scenario analysis, market research, competitive assessment, pricing and market-access evaluation, portfolio strategy, and executive presentations. The role partners with commercial, finance, business development, and analytics stakeholders to guide pipeline decisions and assess asset value.

Own the global B2B content strategy for enterprise audiences, creating content across websites, advertising, whitepapers, case studies, thought leadership, social, video, and sales enablement. Build AI-assisted content production systems with prompt libraries, source-of-truth repositories, agentic workflows, and human review controls. Partner with Product, Sales, and regional Marketing teams to develop enterprise messaging and improve content quality using performance data.

Design, develop, train, deploy, optimize, and monitor production AI and machine learning solutions. Responsibilities include data cleaning and transformation, scalable ML pipeline development, MLOps automation, model testing, infrastructure deployment, and applied work in NLP and computer vision. The role involves end-to-end software product development, cloud platforms such as Azure, agile collaboration, and delivering customized AI solutions for clients.

Develop advanced analytics, statistical, machine learning, and AI solutions for Life Sciences customers across Europe. Integrate Veeva Data Cloud with healthcare datasets, build HCP precision engagement engines, and translate technical models into business insights. Collaborate with product managers, data engineers, operations, and consultants while documenting methodologies, architectures, and validation processes.

Lead AI strategy and adoption initiatives by aligning technical teams, business stakeholders, and senior leadership. Translate business needs into actionable requirements, manage complex delivery risks and dependencies, communicate technical trade-offs, and support AI solutions from proof of concept through production. Drive change management, stakeholder alignment, adoption, and long-term business value across ambiguous, cross-functional environments.

Develop machine learning and analytics solutions using large-scale datasets. Responsibilities include feature engineering, predictive modeling, workflow optimization, customer implementations, production deployments, stakeholder collaboration, technical training, and resolving data-related challenges. The role requires Python, PySpark, machine learning pipelines, MLflow or Airflow, statistical modeling, and strong communication skills, with preferred experience in fraud detection, risk analytics, compliance, cloud environments, and model monitoring.

Analyze production drive-thru voice AI data, build SQL reports and dashboards, monitor KPIs, identify conversation error patterns, and improve NLU models, conversation flows, and LLM behavior through machine learning and rule-based techniques.

Build the Ops AI Platform from the ground up, developing reliable AI agents, agent tooling, internal tools integrations, evaluation systems, safety controls, and human-in-the-loop workflows. Scale automation from dozens to tens of thousands of daily tasks while influencing architecture, code quality, product direction, engineering culture, mentoring, and hiring.