Top Machine Learning Jobs in Barcelona

Leads analytical methodology and evaluation for AI-based healthcare and pharmaceutical workflows. Owns model selection, cohort and HCP segmentation, agent decision logic, and evaluation frameworks. Partners with engineering, data architecture, and governance teams to productionize rigorous analytical methods, identify new data and techniques, communicate insights, and mentor data science and engineering colleagues.

Leads data science methodology and advanced analytics for AI-enabled Medical Affairs workflows. Develops and evaluates statistical, machine-learning, NLP, and AI models using healthcare and real-world data; defines validation, monitoring, and decision frameworks; partners with engineering, governance, and business teams to operationalize solutions. Provides technical leadership, mentorship, documentation standards, and strategic guidance while ensuring privacy, compliance, responsible AI use, and clear communication of analytical findings.

Leads ad hoc and exploratory analyses for medical and business questions across a global Medical Insights organization. Prepares and validates datasets, translates ambiguous stakeholder needs into decision-ready findings, and maintains analytical documentation. Tests new data sources, methods, and AI-enabled approaches through proof-of-concept analyses to guide platform investments and roadmap decisions. Partners with business and product stakeholders while supporting compliant, reproducible use of regulated data.

Design, deliver, and continuously improve scalable production AI solutions using LLMs, RAG, agentic AI, machine learning, and document intelligence. Build cloud-native systems on Azure using software engineering, architecture, DevOps, and MLOps practices. Own AI platforms and models across deployment, monitoring, security, governance, and optimization. Lead technical initiatives, collaborate with product and business teams, mentor engineers, and translate business needs into measurable outcomes.

Lead and develop globally distributed Data and AI teams to deliver production Data/AI solutions. Drive talent development, operational excellence, stakeholder alignment, and responsible AI practices while partnering with Product and Engineering to set priorities, roadmaps, and delivery metrics.

Provides staff-level technical leadership for enterprise AI/ML platform infrastructure. Designs and operates AWS, Kubernetes, serverless, and containerized environments; develops infrastructure-as-code, CI/CD, observability, security, governance, and cost-optimization standards. Supports generative AI workloads, model routing, reliability, scalability, and performance. Partners with engineering, security, and operations teams, mentors engineers, reviews architecture, and drives platform maturity across multiple teams.

Lead multidisciplinary Data and AI teams, client engagements, project execution, and business development for supply chain transformation initiatives. Oversee AI-driven solutions spanning demand forecasting, inventory and network optimization, fulfillment, manufacturing, predictive maintenance, and generative AI. Guide technical strategy, mentor teams, communicate insights to senior stakeholders, develop proposals, and connect analytics initiatives to measurable business value.

Develop AI, advanced analytics, optimization, and machine learning solutions for supply chain planning, fulfillment, manufacturing, and network optimization. Translate client needs into measurable data-driven outcomes, collaborate with data scientists, engineers, and consultants, and support business development through proposals and innovative AI offerings. Work with forecasting, predictive modeling, generative AI, cloud platforms, supply chain software, large-scale data processing, and visualization tools.

Supports data collection, cleansing, exploratory analysis, predictive modeling, reporting, visualization, documentation, and data science innovation projects. Collaborates with Data Scientists and business stakeholders to generate insights and support data-driven decisions. The role is based in Sant Joan Despí, Spain, within Global Demand Planning, and reports to the Global Forecast Analyst Manager.

Supports embedded-device IDE development, Edge AI and vision systems, and medical-grade software engineering. Responsibilities include product verification and validation, software testing, analysis, documentation, process improvement, application development, performance verification, coding, optimization, debugging, research, code reviews, and collaborative project work.

Lead end-to-end ML product roadmaps and initiatives (e.g., Attachment Matcher, Transaction Categorizer). Collaborate with data scientists, engineers, and stakeholders to define, deliver, and monitor ML solutions that drive measurable business value for SMEs across Qonto's platform.

Own the end-to-end lifecycle of production AI, machine learning, and GenAI products across banking operations, fraud, AML, growth, and marketing. Define roadmaps, evaluate models, guide deployment and monitoring, and partner with data scientists and engineers on scalable architectures. Establish standards and self-service tools for safe AI adoption while balancing innovation with security, privacy, compliance, reliability, and auditability in a regulated financial environment.

Own delivery of applied machine learning products from discovery through deployment. Translate operational challenges into technical requirements, coordinate data scientists, ML engineers, and stakeholders, and manage squad backlogs. Focus on automation, document verification, fraud detection, model monitoring, and continuous optimization. Evaluate model performance using metrics such as precision, recall, and F1 score while ensuring security, compliance, and data privacy standards in a regulated banking environment.

Analyze large-scale player data using Python and SQL, engineer features, and develop machine learning models and prediction pipelines to improve game revenue. Conduct experimental design, causal inference, and A/B testing, then present actionable findings to non-technical game teams. The role involves collaborating with global data and game development teams and delivering projects from exploratory analysis through deployed models.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Leads global early-asset forecasting, market insights, commercial diligence, and investment-case development across respiratory and immunology portfolios. Builds patient-based models, evaluates licensing and acquisition opportunities, advises executives, oversees market research, and supports therapeutic-area strategy. The role also advances responsible AI, predictive analytics, automation, and model governance to improve decision quality and portfolio prioritization.

Leads clinical data science delivery and team development, designing advanced statistical models and machine learning methods for clinical research. Oversees data analysis, management, regulatory compliance, and trial optimization while collaborating with clinical, biostatistical, and regulatory teams. Communicates complex insights to senior stakeholders and external partners and drives innovation across clinical development programs.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Lead and mentor a data science team building ML and LLM solutions for cybersecurity. Translate business needs into research and production ML objectives, prioritize roadmaps, collaborate cross-functionally, and drive technical vision, model development, and operationalization for malware detection and threat analysis.

Drive business development and close high-value enterprise deals with global biotech and biopharma accounts across Europe. Own strategic customer relationships, deliver recurring revenue, partner with technical and customer success teams on complex proposals, provide market insights for product direction, build industry credibility, and improve enterprise sales processes and playbooks.

Analyze large-scale mobile user and app datasets to generate insights for Product and Machine Learning strategies. Collaborate with business and technical teams to improve platform and campaign performance, develop dashboards and data-driven processes, conduct statistical analyses and hypothesis testing, and communicate recommendations to stakeholders.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Identifies, designs, deploys, and sustains AI solutions within a Wind Engineering subsystem. Partners with engineering leaders to prioritize use cases, integrates AI into engineering workflows, ensures governance and cybersecurity compliance, drives adoption, and monitors solution performance and value. The role develops applications, agentic workflows, copilots, automation, predictive models, and knowledge systems while contributing reusable patterns and innovations to the broader AI community.