Top Machine Learning Jobs in Cologne

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.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Develop, design, test and implement imaging and 3D reconstruction algorithms using C++ and Python. Analyze real data, optimize for high-performance computing, integrate algorithms into applications, and collaborate across algorithms, physics, and electronics teams while participating in design and code reviews.

Own post-sale customer lifecycle for Portuguese utility accounts: onboard, embed Neara into workflows (vegetation management, clearance analysis, CAPEX), run QBRs, monitor health scores, drive renewals and upsell, and align product roadmap with local market needs.

Design and scale predictive ML systems for VIS.X, taking end-to-end ownership of various high-impact ML initiatives and shaping the ML architecture.

Design, build, and operate ML platform systems to train, evaluate, deploy, and serve generative models. Improve reliability, scalability, observability, and cost-efficiency of GPU and cloud workloads. Build internal tools and agent-friendly workflows, collaborate with researchers and engineers, and drive platform architecture and automation.

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.

Build and operate ML infrastructure powering model training, evaluation, deployment, inference, observability, and continuous improvement. Improve reliability, scalability, latency, throughput, and cost efficiency. Provide tooling and pipelines enabling fast experimentation, reproducible ML workflows, benchmarking, monitoring, and platform primitives consumable by AI engineers and researchers.

Design, prototype, and deploy ML models for personalization in mobile games. Implement and evaluate algorithms (including RL and generative methods), run A/B tests, convert prototypes to production via MLOps, monitor player data, and collaborate with product, engineering, and analytics teams to improve retention and monetization.

Lead applied ML efforts to embed LLMs and agentic workflows into production, focusing on anti-fraud, automated evaluation, and operational automation. Design reusable architectures, implement observability and A/B tests, partner with delivery and clients, and set engineering standards for prompts, agents, pipelines, and CI/CD.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Build ML prototypes and data pipelines, collaborate with researchers and product engineering to integrate models into Fusion, containerize and deploy applications, implement best practices, and document architectures to accelerate research-to-production workflows.

Design and build end-to-end ML systems for fraud detection: develop and deploy models, create production-ready features and real-time data pipelines, integrate with backend services, and ensure monitoring, security, and observability.

Lead design and productionization of ML pipelines, evaluation systems, and AI agents for cybersecurity. Own experimentation-to-production lifecycles, integrate ML across product teams, scale ML infrastructure, optimize agent performance, and mentor junior engineers while collaborating with CTO and product leadership.

Work on Zendesk Explore to build and annotate golden datasets, implement LLM-as-Judge evaluation logic in Python within the Braintrust platform, and iterate automated metrics against human feedback to improve AI agent quality. Contribute across front-end and back-end of the SaaS product.

Lead design and deployment of expert and generalist robot policies using hierarchical planning, reinforcement learning, and hybrid methods. Build real and simulated datasets, guide sensor selection and integration, collaborate with embedded and hardware teams, and contribute to AI platform architecture, CI/CD, and MLOps. Mentor engineers and drive technical roadmap for production-ready robotics solutions.

Design, train, fine-tune, distill, and deploy LLM-based and agentic ML models. Build and operate agent workflows in Python, run evaluation and benchmarking, select models and cost strategies, and manage production monitoring and observability for model performance and reliability.

Own, design, build, and operate distributed, high-load backend services and data pipelines for ML infrastructure. Partner with ML and product teams to deliver self-service, reliable model serving, storage, and deployment components; ensure observability, scalability, and performance while driving technical decisions and continuous improvement.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Build and ship state-of-the-art speech systems end-to-end: model architecture, training (pre-train/fine-tune/post-train), evaluation, tooling, data curation, production inference, and safety/abuse mitigation for voice conversion and related tasks.

Lead end-to-end architecture and integration of battery insights algorithms into production embedded systems. Define modular architectures, drive algorithm development (signal processing, ML, modeling), translate algorithms to real-time software, guide cross-functional teams, mitigate risks, and mentor engineers to ensure scalable, maintainable, production-ready BMS solutions.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Build and scale ML-powered search and RAG capabilities at Zendesk scale. Collaborate with product, ML scientists, and engineers to design, deploy, and maintain reliable production services, improve relevance and ranking, optimize indexing pipelines, and mentor teammates while applying experiments and analysis to drive decisions.

Own and evolve ClickHouse's Python connector and SDK ecosystem, build integrations with orchestration and transformation tools, drive AI/LLM integration strategy for RAG and feature pipelines, engage with open-source community, and collaborate across product and engineering to prioritize real-world data workflows.