Top Machine Learning Jobs in Cologne

Develops and integrates radiating components and passive radiation-pattern structures for multiband base-station antennas. Responsibilities include 3D and 2D electromagnetic simulation, prototype RF measurements, performance optimization, troubleshooting, technical documentation, and supporting the product lifecycle through mass production. The role collaborates with international cross-functional teams and requires relocation to Rosenheim, Germany.

Manage multiple intralogistics and robotics deployment projects from specification through operational handover. Coordinate multidisciplinary teams, track performance and budgets, report to stakeholders and clients, and manage project risks. The role requires customer engagement, German and English fluency, technical understanding, and travel to European warehouses up to 50% of the time.

Leads JetBrains Air’s data science, analytics, and data engineering functions. Builds and develops multidisciplinary teams, establishes scalable data foundations and metric frameworks, drives experimentation, and delivers insights for product, marketing, acquisition, retention, and monetization. Partners with engineering and leadership on data strategy, infrastructure, machine learning capabilities, privacy, security, and responsible data use.

Develop and demonstrate Physical AI solutions on NVIDIA and hyperscaler platforms. Advise customers and partners, optimize GPU-accelerated training and inference pipelines, conduct proof-of-concepts, and collaborate with Engineering, Product, Sales, and Marketing teams. Build technical relationships, gather customer feedback, support enterprise architecture integration, and evangelize accelerated computing and generative AI to developers, researchers, executives, and technical leaders.

Manage relationships with AI-native startups, scaleups, and labs in Germany. Understand customer goals, develop AI solution strategies, coordinate NVIDIA technical and business resources, drive adoption of AI platforms, build ecosystem partnerships, develop customer references, identify emerging use cases, and represent NVIDIA at industry events. The role requires deep AI industry knowledge, strategic planning, cross-functional collaboration, and familiarity with NVIDIA’s software, server, and cloud stack.

Lead NVIDIA’s developer relations strategy across EMEA Smart Spaces and Local Government ISV ecosystems. Build partner relationships, deliver technical enablement, support integrations, guide startups, and drive adoption of NVIDIA AI and computing platforms. Collaborate with sales, solution architecture, engineering, product, and marketing teams; translate partner needs into product feedback; demonstrate AI solutions; and influence technical and commercial growth. The role requires deep AI/ML and generative AI expertise, technical collaboration skills, and experience scaling developer programs.

Advise city governments across the Americas on designing, prototyping, and launching digital services. Guide system and data architecture, DevOps, infrastructure, AI and machine learning decisions, rapid prototyping, and end-to-end product delivery. Work hands-on with multidisciplinary teams while coaching clients and influencing technical decisions without direct authority. The role requires senior technical leadership, strong communication, and professional English plus Spanish and/or Portuguese.

Lead and develop machine learning, LLM, and automation projects using large-scale network and device telemetry. Clean and transform data, engineer features, build models for anomaly detection, forecasting, and optimization, and deliver reproducible analyses with Python and SQL. Collaborate with engineering and product teams to deploy, integrate, and monitor models in production while contributing to MLOps workflows.

Develop, debug, test, and optimize machine learning models, training pipelines, inference systems, numerical methods, and supporting infrastructure. Build reproducible Python workflows, diagnose computational issues, validate implementations, use coding agents, and document technical findings. This remote role requires approximately 15 hours per week and rapid task availability.

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.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Own the vision, roadmap, commercialization, and executive strategy for Kaltura’s AI-powered learning and enablement platform. Lead 0-to-1 MVP delivery and post-MVP expansion across AI course generation, conversational tutoring, personalized learning, simulations, and enterprise foundations. Integrate acquired products and agentic AI capabilities while partnering with engineering and GTM teams on architecture, pricing, packaging, positioning, and enterprise sales. The role requires deep B2B SaaS product leadership, enterprise learning platform experience, production AI/ML delivery, and strong technical and commercial judgment.

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.

Own the strategy and execution of integrated B2B marketing campaigns for enterprise AI solutions and a self-service platform. Drive pipeline, product adoption, acquisition, and category awareness through ABM, content, events, paid media, lifecycle, social, partnerships, PR, and sales activation. Coordinate internal teams and agencies, optimize messaging and conversion paths, and measure performance across account engagement, pipeline, product adoption, and awareness.

Provides statistical leadership for Medical Affairs, real-world evidence, comparative effectiveness, HTA, and HEOR activities. Designs and analyzes observational, clinical, Phase IV, and indirect treatment comparison studies; evaluates data quality, bias, uncertainty, and applicability; supports evidence-generation strategies, reimbursement submissions, publications, and scientific communications. Collaborates with statistics, epidemiology, RWE, medical, and external scientific teams to translate complex analyses into actionable conclusions.

Drive Azure and AI sales across the DACH market, managing opportunities from qualification through close and building pipeline against Azure consumption, workload adoption, and AI deployment targets. Lead customer conversations, technical sales cycles, workshops, proofs of concept, and solution envisioning. Translate customer challenges into secure Azure architectures, guide responsible AI adoption, and build trusted relationships with senior decision makers while collaborating with account executives, engineers, partners, and customer success teams.

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.

Design and deliver internal systems, Go services, APIs, data models, dashboards, visualizations, and LLM-powered workflow features. The role includes writing specifications, estimating work, code reviews, testing, documentation, security, performance, accessibility, and collaboration with nontechnical stakeholders. The engineer will handle sensitive people data, mentor colleagues, and improve team practices in a globally distributed environment.

Build and own pricing data science capabilities, including demand and elasticity models, forecasting, willingness-to-pay models, pricing optimization, experimentation, and causal analysis. Deploy and monitor machine learning systems, establish governed pricing metrics, and partner with Commercial, Finance, and Product to improve margin and revenue. Set analytical standards for pricing decisions and build reusable analytical capabilities.

Own the anti-fraud product roadmap across signup, payments, account protection, compute-abuse prevention, risk scoring, manual review, customer verification, and appeals. Partner with Trust & Safety, Engineering, Data Science, Analytics, and Legal to define detection rules, models, incident responses, experiments, and compliance processes. Monitor fraud losses, chargebacks, false positives, review throughput, and detection speed while minimizing customer friction.

Lead neuroscience R&D for EEG-based BCI: develop signal-processing and classification algorithms, design/execute human-subject experiments, integrate neural data with hardware/software, validate and ensure regulatory and ethical compliance, and mentor junior researchers.

Manage NVIDIA’s relationships with AI-native startups, scaleups, and labs. Identify customer needs, develop AI solution strategies, drive adoption of NVIDIA platforms, coordinate cross-functional and partner resources, create customer references and case studies, develop emerging use cases, and represent NVIDIA at industry events. The role requires strategic market analysis, AI business development expertise, ecosystem relationships, and strong communication skills.

Lead and develop Customer Success Managers across Southern Europe, especially France and Spain. Ensure enterprise customers successfully launch and derive measurable value from the product, build executive and technical stakeholder relationships, translate product capabilities and performance data into business outcomes, establish scalable customer success processes, and handle complex escalations.

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.

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.