Top Machine Learning Jobs in Barcelona

Develop quantitative models for sports pricing, odds compilation, and risk management. Analyze large datasets, identify trading insights, deploy models with engineers, monitor performance, conduct back-testing, and continuously improve model accuracy. Collaborate with data scientists and engineers to align models with business needs. The role requires Python, sports betting market knowledge, statistical modeling, and experience with odds and margin optimization.

Manage and grow a European client portfolio for a mobile and CTV advertising platform. Responsibilities include monitoring and optimizing user acquisition campaigns, achieving client KPIs, identifying business development opportunities, delivering data-driven insights, promoting product features, leading client meetings and business reviews, and representing Smadex externally.

Develop and deploy machine learning models for retrieval, reranking, indexing, search relevance, crawling, and content understanding. Build large-scale embedding and retrieval systems, evaluation pipelines, and quality metrics for agent-native search. Integrate models into high-throughput production services while balancing latency, quality, and cost. Collaborate on architecture and product decisions involving search, retrieval, and LLM-based systems.

Build production AI-enabled search tools and Python backend services. Develop LLM applications, evaluation frameworks, relevance metrics, APIs, CI/CD environments, and data integrations. Assess quality, latency, cost, security, and reliability while collaborating with product, design, data science, and search teams. Translate prototypes into user-focused production releases using appropriate combinations of LLMs, traditional machine learning, deterministic rules, and human review.

Lead product marketing for the French market by developing localized positioning, messaging, sales enablement, launch campaigns, and customer insights. Partner cross-functionally with product, sales, marketing, engineering, design, communications, and partnerships teams. Track adoption and market performance, identify growth opportunities, and lead product marketing strategy for a major Paris customer event. The role requires French and English fluency, B2B technology marketing experience, and travel within France and internationally.

Own the end-to-end strategy, roadmap, and outcomes for a network intelligence and security product serving ISP customers. Partner with engineering, data science, security, and compliance teams on architecture, ML evaluation, privacy, and production delivery. Conduct customer discovery, analyze market and threat trends, define requirements and metrics, build business cases, and represent the product to technical customers, executives, sales, marketing, and public audiences.

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.

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.

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.

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 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.

Build Solidgate’s ML/AI function from the ground up, developing real-time payment routing models and production pipelines. Own the full ML lifecycle from experimentation and A/B testing through deployment, while solving modeling challenges involving large-scale transactional data. Partner with Product, Engineering, and Data Platform teams to deliver measurable improvements in conversion, approval rates, losses, and revenue.

Own the lifecycle of production machine learning systems, including deployment, monitoring, refactoring, testing, versioning, and CI/CD. Collaborate with data scientists and engineers to establish reliable interfaces and data flows. Provide technical guidance, review code, and promote scalable, maintainable engineering practices while productionizing models from notebooks.

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.

Design and build production-grade agentic AI and ML solutions, including LLM and RAG systems, end-to-end data architectures, multi-agent workflows, guardrails, monitoring, and automated actions. Collaborate with engineering, project, and client teams to prioritize use cases, explain technical decisions, and deliver prototypes through production. Contribute to real-time data pipelines, AI evaluation, governance, and team knowledge sharing.

Develop and deploy AI-powered 3D graphics solutions for desktop, web, VR, and AR applications using the Evergine engine. Build computer vision and spatial computing models, including NeRF, Gaussian Splatting, point-cloud processing, SAMv2, and GroundingDINO or FlorenceV2. Design scalable algorithms, integrate machine learning frameworks, and collaborate with research and engineering teams in an Agile environment.

Own the Mapping & Content product domain end to end, including strategy, prioritization, economics, and outcomes. Lead efforts to match hotel rooms and content across suppliers, languages, and formats with high accuracy and coverage. Partner closely with engineering and data science teams on entity resolution, search, matching, catalog, identity, recommendation, and content-quality problems. Identify high-value failures, make trade-offs between precision and coverage, and help build Nuitee’s product organization in a fast-moving environment.

Lead research on interactive avatar models and real-time generative AI video agents. Set research direction, advance multimodal perception and reaction generation, post-train diffusion models, build evaluation frameworks, define datasets, and guide research-to-production efforts. Mentor researchers and influence technical decisions across research, data, and engineering teams.

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.

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.

Design, deploy, and maintain scalable AI/ML systems and MLOps, LLMOps, and AgentOps pipelines. Build batch and real-time infrastructure, conduct large-scale testing, monitor models, and integrate AI solutions into business applications. Collaborate with scientists, engineers, and product teams using Python, Azure, MLflow, and Databricks.

Lead the design and delivery of production conversational AI systems, including multi-turn agents, LLM reasoning loops, and multi-agent orchestration. Build and benchmark end-to-end solutions using Python, Azure, MLflow, and Databricks. Drive AI initiatives, mentor junior scientists, lead cross-functional teams, collaborate with customers, and advance best practices in generative and agentic AI for complex enterprise domains.