Top Machine Learning Jobs in Cologne

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.

Design, build, test, and improve plugin-based forecasting models from business need through production deployment. Own ML and modeling initiatives end to end, evaluate performance across customer environments, distinguish generalizable solutions from customer-specific requirements, and use AI-assisted engineering tools. Work autonomously with customers and cross-functional stakeholders in a distributed international team, with occasional travel.

Owns an AI-native fintech product vertical end-to-end, including strategy, roadmap, technical design, delivery, metrics, and launch. The role covers APIs, data flows, ledgering, reconciliation, security, scalability, regulatory requirements, financial rails, AI evaluation, safeguards, and cross-functional alignment across engineering, compliance, operations, treasury, finance, and design.

Lead and expand a strategic financial-institution account across the DACH region. Build C-suite relationships, identify business challenges, develop tailored enterprise solutions, structure and negotiate multi-million-dollar SaaS deals, and execute joint business plans to achieve growth and profitability targets. Act as the client advocate internally, influence product strategy, collaborate across sales and delivery teams, and serve as a trusted advisor on analytics, fraud, risk, lending, and decisioning technologies. Travel may reach 50%.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Build and maintain high-performance differentiable simulation software for open and closed quantum systems using Julia and Python. Develop model-learning, parameter-inference, neural-network PDE-solving, and optimisation methods. Benchmark research methods, improve performance and reliability, collaborate with university faculty, publish results, supervise students and interns, and contribute to teaching materials.

Lead technical discovery, solution design, demonstrations, and proof-of-concept support for Anaconda’s AI and data science platform across EMEA. Partner with account executives on complex deals, create reusable technical content, represent customer needs to product and engineering, and coordinate with cross-functional teams through implementation handoff. Manage multiple opportunities independently while improving technical win rates, deal expansion, sales velocity, and regional pipeline development.

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 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 Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

Assess and evaluate AI-generated technical content, craft and answer advanced ML/AI questions, identify errors and hallucinations, rank responses by correctness and clarity, and provide precise feedback to improve model performance.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

Lead AI and machine learning architecture, mentor teams, drive innovation, and evaluate tools for AI systems development across global teams.

Research and investigate global fraud trends using BioCatch data and external sources. Produce data-driven insights, visualizations, written thought leadership, dashboards, and presentations. Build repositories and automate analysis workflows while applying fraud expertise and AI tools to validate findings and support sales and marketing.

Manage various AI/ML projects from concept to development, coordinating teams, managing risks, and ensuring project goals are met. Responsible for documentation and financial planning.

Build production-grade ML and MLOps infrastructure for cloud-native environments. Responsibilities include orchestrating Kubeflow pipelines, training and fine-tuning GPU-based models and LLMs, tracking experiments with MLflow, developing Python tooling, processing large datasets, and maintaining CI/CD, testing, security, and clean-code standards within Kubernetes.

Build and scale MLOps infrastructure that moves machine learning models from development to production. Responsibilities include model serving, automated training and deployment, CI/CD, monitoring, drift detection, rollback mechanisms, streaming feature pipelines, and agentic workflow integration. The role requires collaboration with data science, platform, and product teams to deliver reliable, observable, low-latency ML systems.

Own end-to-end delivery of specific ADAS features (e.g., TFL, High Beam Assist, Blind Spot Monitoring). Define feature scope, set performance targets, align OEM requirements with internal roadmaps, collaborate with engineering/product/OEM teams, participate in testing/validation, and mitigate risks to ensure features meet specs and production readiness.

Owns the end-to-end data processing and dataset lifecycle for robotics AI/ML systems. Designs scalable pipelines that convert raw sensor recordings into reliable training, validation, benchmarking, and regression datasets. Implements data quality checks, versioning, lineage, reproducibility, distribution, and infrastructure integration across cloud and compute environments. Develops dashboards monitoring data quality, scenario coverage, labeling, and ML readiness while collaborating with AI/ML, perception, validation, and platform engineering teams.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Lead design and build of a scalable internal data platform and central warehouse, own company-wide data architecture, mentor engineers, implement governance, observability, and quality controls, and enable AI adoption by creating pipelines and infrastructure for autonomous agent and ML use cases.

Lead technical presales engagements for an AI-driven commerce platform: run discovery sessions, design composable solution blueprints, build demos and prototypes, author technical RFP/RFI responses, and collaborate with GTM, Product, and Engineering to close enterprise deals across the DACH region.

Own Go-To-Market for performance media buyers: build relationships, close and onboard clients, represent Kayzen at industry events, advise prospects on product strategy, and funnel market feedback across teams.