Top Machine Learning Jobs in Hamburg

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.

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.

Build, operate, and improve production services that deliver machine learning models within exocad’s AI Design product. Responsibilities include model serving APIs, containerized deployments, versioning, staged rollouts, rollback automation, infrastructure provisioning with Terraform and AWS, monitoring, observability, and performance tuning. The role also contributes to training and labeling infrastructure and requires collaboration across engineering, product, and global teams, including work across AWS global and China environments.

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 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.

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.

Build and maintain scalable backend systems that integrate real-time data, machine learning, and optimization models to optimize renewable and battery storage operations. Own architecture through deployment and maintenance, collaborate with engineers and data scientists, troubleshoot performance issues, and improve monitoring, reliability, and system performance.

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 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 research and development of AI accelerator/NPU IPs for embedded products. Architect RTL and hardware to meet power/performance/area targets, collaborate with compiler and software teams, model systems, and mentor cross-functional teams toward chip tape-out.

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.

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.

Design, implement and optimize Retrieval-Augmented Generation platforms, document ingestion, embeddings and vector search. Build agentic workflows with LangGraph, integrate LLM providers and cloud services, containerize and automate infrastructure, implement testing and validation for RAG systems, and develop REST APIs and data models.

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.

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.