Top Machine Learning Jobs in Cologne

Lead the architecture, development, deployment, and operation of production-grade machine learning systems for recommendations, personalization, ranking, and product discovery. Translate ambiguous business needs into scalable solutions, oversee the full ML lifecycle, improve reliability and observability, and guide technical decisions. Provide technical leadership through mentoring, design reviews, engineering standards, and collaboration with product, engineering, data, AI, and business stakeholders.

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.

Designs, develops, and deploys production-grade machine learning systems focused on LLMs, Generative AI, and deep learning. Responsibilities include building scalable ML libraries and research workflows, optimizing distributed training and inference, improving experiment tracking and model lifecycle management, and bridging research prototypes with robust implementations. The role requires collaboration with data science and platform teams, strong software engineering practices, cloud and MLOps expertise, and mentoring across the technical team. Biomedical, clinical, or omics data experience is preferred.

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.

Develop and maintain high-load, real-time personalization services for global e-commerce platforms. Improve shopper experiences and business KPIs through applied machine learning, user interaction modeling, personalization and recommender systems, hypothesis validation, and A/B testing. Collaborate with engineering teams and business stakeholders to research, deliver, and communicate personalization capabilities and outcomes.

Leads consultative discovery, technical qualification, solution design, executive presentations, and sales campaigns for AspenTech industrial software. Partners with sales teams to align technology with measurable customer value, develops industry-specific messaging, engages senior executives, provides product feedback, coaches sales personnel, and represents the company at industry events. Requires extensive industrial software or solution consulting experience, technical domain expertise, multilingual fluency, and European work authorization.

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.

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.

Own the full machine learning lifecycle, from exploration and prototyping through production deployment and operations. Build ML models, training and evaluation pipelines, experiment tracking, model monitoring, and drift detection using Python, MLflow, AWS, and SQL. Collaborate with data engineering, contribute to reliable data pipelines, maintain high engineering standards, and mentor engineers and data scientists. Recommender systems, AdTech, deep learning, GCP, and Snowflake experience are advantageous.

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.

Develops new business and revenue across DACH banking and financial-services accounts. Owns strategic account plans, complex enterprise sales, pipeline forecasting, deal closure, customer and partner relationships, and executive-level engagement. Collaborates with business development, sales engineering, account management, and leadership teams to drive adoption, customer success, and revenue growth. Represents the company at relevant events and supports go-to-market initiatives. Frequent domestic and infrequent international travel is required.

Own the DACH market launch for Nebuly, generating pipeline through an established enterprise network and managing the full B2B SaaS sales cycle. Target mid-market and enterprise accounts with €100K+ ACV potential, navigate complex stakeholder groups, build business cases, negotiate, and close deals. Represent Nebuly at market events, collaborate with sales engineering and partners, and develop system integrator and advisory relationships. The role requires German and English fluency, strong enterprise relationships, technical sales ability, and experience closing large contracts without inbound or SDR support.

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.

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.

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 the Smart Risk Engine for a B2B mobility and payments platform. Develop statistical, machine learning, and AI models; build cloud-based end-to-end ML pipelines; manage training, calibration, backtesting, monitoring, drift detection, and explainability. Create credit-risk use cases including early-warning systems, loss databases, exposure steering, and portfolio insights. Partner with senior stakeholders, translate analytics into business recommendations, and mentor Data Scientists. German and English fluency are required.

Manage and expand enterprise accounts across the EMEA region through consultative selling, strategic account planning, renewals, customer retention, and new business development. Build relationships with senior executives, sell complex software and analytics solutions, maintain CRM records, monitor market trends, and collaborate with Customer Success, Marketing, SDRs, and Product teams. German and English fluency are required, with travel to events and customer meetings.

Sell Zscaler Zero Trust Branch solutions across DACH, owning regional quota, building account-based strategies, partnering with solution engineers, engaging C-suite and technical stakeholders to close complex deals for SD-WAN, data security, and advanced network products.

Leads NVIDIA’s developer relations strategy for manufacturing and industrial ecosystems across EMEA. Advises developers, software vendors, startups, OEMs, and integrators on AI and accelerated-computing solutions, including robotics, digital twins, simulation, computer vision, and predictive maintenance. Creates technical enablement assets, supports onboarding and integration, analyzes ecosystem opportunities, gathers developer feedback, and collaborates with engineering, product, marketing, sales, and partners to drive adoption and production-ready implementations.

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.