Top Machine Learning Jobs in Madrid

Develop, validate, deploy, and maintain machine learning solutions for medical device manufacturing and CAD/CAM systems. Build deep learning and multimodal AI models using computer vision, 3D geometry, meshes, images, and structured data. Integrate models into production systems, conduct performance analysis, maintain ML workflows, and translate research into practical manufacturing applications while collaborating with engineering, manufacturing, and product teams.

Owns full-cycle recruiting for commercial, operational, and technical roles across global markets. Responsibilities include sourcing passive candidates, building pipelines for hard-to-fill positions, advising hiring leaders, coordinating interviews and offers, maintaining accurate Greenhouse data, and improving hiring processes through systems thinking, data, AI, and automation.

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.

Designs and delivers production credit analytics and machine learning platforms for regulated financial-services clients. Responsibilities include architecture, Python and SQL development, reproducible ML workflows, MLOps, data quality, model governance, Snowflake application reliability, monitoring, access control, and technical leadership. The role works directly with clients and modelling specialists to productionize credit-risk models, support multiple portfolios, and evaluate responsible uses of LLM-assisted analytics.

Designs, builds, and maintains scalable data and machine learning infrastructure, pipelines, databases, integrations, self-service tools, and MLOps workflows. The role includes platform expansion, observability, security, data quality, performance optimization, infrastructure as code, CI/CD, documentation, and collaboration with data scientists, analysts, and stakeholders. Strong experience with cloud platforms, Kubernetes, containerization, databases, big data, orchestration, and AI-assisted development is required.

Own the Vault product roadmap and lifecycle for a sports media asset platform. Lead discovery with customers, define solutions for content ingest, storage, integrations, AI enrichment, metadata, search, rights, and asset monetization. Partner closely with engineering, sales, and customer success, attend customer events, and prioritize product bets that help sports organizations maximize the value of extensive media archives.

Corti is seeking passionate individuals to join their talent community, shaping the future of healthcare through AI technologies. Opportunities include roles in AI technology, machine learning, and software development, focusing on reducing errors and improving patient outcomes.

Designs and operates scalable production data infrastructure for high-volume athlete, sensor, and time-series data. Builds streaming ingestion, graph models, low-latency ML feature systems, evaluation and monitoring infrastructure, and multi-tenant architectures. Ensures data provenance, lineage, observability, reliability, and correctness while collaborating with ML engineers, data scientists, sport scientists, and domain experts. Requires expertise in Python, SQL, streaming technologies, AWS, graph data modeling, causal inference, AI evaluation, and telemetry systems.

Build and productionize machine learning systems for classification, retrieval, ranking, personalization, and evaluation. Develop models and datasets, optimize accuracy, latency, cost, and reliability, and maintain training, inference, and deployment pipelines. Monitor production performance, debug issues, and ship improvements based on real-world feedback. Collaborate with engineering and product teams to deliver reliable AI-powered features.

Optimize large-scale ML inference systems for throughput, latency, cold-start time, and cost per token. Tune SGLang, vLLM, and TensorRT-LLM across GPUs and distributed clusters; improve batching, scheduling, KV caching, quantization, weight loading, JIT caching, and multi-node inference. Profile and instrument systems to validate performance gains, eliminate storage and networking bottlenecks, and establish technical direction through reproducible benchmarks and experiments.

Build reproducible bioinformatics workflows with Nextflow, develop cloud and HPC data infrastructures, and deploy scalable pipelines for genomics and other scientific datasets. Engage customers to define requirements, deliver projects, provide training, document solutions, and communicate progress. The role also collaborates with internal teams and applies expertise in programming, omics analysis, cloud architectures, and production bioinformatics software.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Build and maintain Ergodic AI’s AI platform across back-end and front-end systems. The role involves developing with Python web frameworks and JavaScript/TypeScript, integrating with client technology stacks and workflows, applying AI, ML, and LLM concepts, and contributing to DevOps, cloud deployment, testing, and infrastructure practices. As an early-stage hire, the engineer will help shape the product and solve complex data and AI challenges.

Develop AI-augmented blockchain risk and threat intelligence systems, including autonomous monitoring and investigation workflows. Lead forensic investigations into crypto theft, fraud, and market manipulation. Research post-quantum cryptography and quantum threats to blockchain security, evaluate migration paths, and collaborate with internal teams, external stakeholders, and academic institutions to deploy emerging security tools.

Supports Account Executives throughout the sales cycle by leading technical discovery, solution design, demonstrations, workshops, proofs of concept, evaluations, and technical validation. Translates engineering, simulation, HPC, AI, and infrastructure requirements into Rescale recommendations, documents opportunities in Salesforce, resolves technical risks, supports negotiations, and ensures effective post-sale handoffs across Northern Europe.

Build and enhance RAG systems, AI components, backend services, data pipelines, and REST APIs under senior guidance. Write production code, tests, evaluation harnesses, documentation, and runbooks. Support AWS deployments using containers and CI/CD, investigate model failures, and contribute to architecture and client handovers. The role offers increasing ownership of technical components and decisions while working in distributed, customer-facing delivery teams.

Design, develop, evaluate, and deploy deep learning and embedding-based models for real-time advertising prediction, bidding, and optimization. Work with massive, high-cardinality datasets, machine learning pipelines, feature infrastructure, and production model monitoring. Collaborate with data scientists, ML engineers, software engineers, and data engineers to translate experiments into scalable solutions and measure their impact on campaigns and business outcomes.

Develop new business and revenue from banking and financial-services accounts in Spain. Manage strategic account plans, complex sales opportunities, partnerships, forecasting, pipeline, and deal closure. Collaborate with business development, sales engineering, account management, and executive leadership to drive technology adoption, customer success, and revenue growth. Represent the company at relevant events and communicate business value to senior and C-level stakeholders.

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.

Own enterprise customer relationships from kickoff through adoption and ongoing value realization. Lead success plans, QBRs, executive conversations, escalations, stakeholder alignment, training, and strategic recommendations. Translate technical capabilities and performance data into business outcomes such as revenue, conversion, and customer experience. Partner with Account Management to support retention and expansion while improving scalable onboarding and support processes.

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 research across the full machine learning lifecycle, including ideation, model design, implementation, evaluation, training, inference optimization, and deployment. Develop novel machine learning methods and foundation models, establish evaluation protocols, scale training with GPUs or TPUs, and improve research engineering infrastructure for rapid experimentation.

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 production optimization of LLM and VLM inference systems, improving latency, throughput, memory efficiency, GPU utilization, quality, reliability, and cost. Deploy and benchmark inference engines, build compression and quantization workflows, implement acceleration techniques, diagnose serving bottlenecks, and collaborate with kernel, platform, research, product, and customer teams.