Top Machine Learning Jobs in Seville

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.

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.

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.

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.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

Own end-to-end talent acquisition and headhunting for Arago’s technical hiring across France, North America, and Israel. Partner with the CEO, CTOs, and engineering leaders to define roles, source and close senior engineers, scientists, and technical leaders across hardware, optical, software, and research. Build proactive sourcing pipelines, improve interview processes and hiring standards, monitor pipeline health and hiring velocity, and communicate progress to leadership.

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.

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.

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 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.

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.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

The Principal Product Manager will drive the strategy for AI-based pricing and reconciliation across travel services, manage model lifecycles, and ensure operational integrity in real-time financial data processing in collaboration with cross-functional teams.

Lead a cross-functional team building ML-powered marketplace catalogue systems, including LLM enrichment, categorization, translation, moderation, embeddings, recommendations, and evaluation infrastructure. Own the product data pipeline, ML roadmap, quality SLAs, experimentation, reliability, technical debt, and incident response. Define OKRs, lead cross-team initiatives, hire and develop engineers, and connect catalogue quality improvements to conversion, discovery, and merchant growth.