Top Machine Learning Jobs in Cologne

Own end-to-end delivery of ADAS features (AEB, LKA, ESA, AES, BSM), align OEM requirements with internal roadmaps, specify features, collaborate with engineering and product teams, support testing/validation, mitigate risks, and maintain strategic feature roadmaps toward higher autonomy.

Support sales by conducting technical discovery, delivering customized demos and POCs, designing solutions with customers, responding to RFIs/RFPs, coordinating internal teams, answering security questionnaires, and acting as a trusted advisor on fraud/AML and financial crime prevention.

The Head of Education & Community at Siena is responsible for developing product education, onboarding programs, community events, and market education resources. This role involves creating curricula, conducting workshops, and supporting sales with training and demos while leveraging AI for teaching efficiency.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

Drive NVIDIA revenue growth within pharma, biotech and life sciences in DACH by building healthcare-specific solutions, cultivating strategic accounts and partner channels, leading sales execution, and coordinating cross-functional teams to expand pipeline and close enterprise deals.

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.

Leads development and deployment of expert and generalist robot policies for industrial automation. Designs task-planning and policy-learning systems using hierarchical planning, reinforcement learning, and hybrid methods. Develops simulation and real-world datasets, guides sensor integration, collaborates across embedded and hardware teams, and advances AI platform architecture, CI/CD, and MLOps. Provides technical leadership, establishes roadmaps, mentors engineers, and deploys robotics algorithms into real-world systems.

The Senior Technical Product Manager will shape product strategy for Constructor's platform, ensuring capabilities support scalability and security for learning applications.

Lead presales technical strategy for enterprise accounts: architect AI-infused planning solutions, run discovery sessions, deliver tailored demos and proofs-of-concept, coordinate cross-functional teams, and develop multi-year technology roadmaps to drive financial transformation.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

Design, build, and operate production-grade generative AI systems: multi-agent orchestration, end-to-end RAG pipelines, LLM evaluation and integration, scalable AWS-based AI infrastructure, CI/CD for models, observability, data validation, and evaluation frameworks to ensure quality, attribution, and resilience.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Create and review realistic, locale-specific robotics and natural sciences tasks in the target language, apply scoring rubrics, evaluate AI and human responses for accuracy and regulatory alignment, and produce high-quality gold-standard solutions and feedback.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

Lead and grow a ~50-person ML organization, prioritize high-impact AI projects, set ML technical direction and standards, ensure delivery and measurable business value, and collaborate across products to translate AI research into product innovations.

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.

Own and ship core ML subsystems end-to-end for a proactive long-horizon AI assistant: data prep, training, evaluation, inference, debugging production failures, iterating rapidly, and mentoring other engineers while meeting latency, reliability, and safety constraints.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Seeking a Senior/Lead Machine Learning Engineer with expertise in inference optimization, model acceleration, and high-performance systems for developing real-time multimodal models.

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.