Top Artificial Intelligence Jobs in Hamburg

Create end-to-end game and marketing assets using AI-assisted workflows. Build and maintain ComfyUI pipelines, combine AI outputs with Illustrator/Photoshop/After Effects, animate assets, ensure visual consistency with the Empire style, test and integrate new AI tools, and collaborate with art, product, CRM, design, and development teams.

Leads NVIDIA’s AI Physics sales strategy across EMEA, driving market growth, strategic customer adoption, ecosystem partnerships, pipeline development, and production-scale success across engineering-intensive industries. Builds executive relationships, identifies high-value use cases, communicates market insights internally, and promotes AI Physics, simulation, digital twin, and accelerated computing solutions.

Owns NVIDIA’s EMEA go-to-market motion for its Agentic AI Platform, driving customer and partner adoption across enterprise, cloud, public sector, defense, and regulated environments. Translates complex AI, security, licensing, legal, and deployment requirements into sales plays, reference architectures, field guidance, workshops, and executive content. Coordinates product, engineering, legal, security, sales, and ecosystem teams while sharing market requirements and connecting technical adoption to revenue outcomes.

Lead and operate the sales process for AI and robotics solutions: identify and qualify leads, run initial customer discussions, manage pipeline and proposals, coordinate with technical teams, hand over projects to project management, and collect market feedback to shape product and business development.

The Backend Engineer will develop and maintain features for Restream web services, design backend APIs, ensure infrastructure resilience, and contribute to a collaborative team environment.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.

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.

Build and maintain the marketing automation backbone and AI agent integrations. Implement event tracking, data pipelines, CRM customizations, and cross-channel workflows. Prototype agent infrastructure, connect APIs/webhooks, automate go-to-market tasks, and produce analytics and attribution to support growth and experimentation.

Build and ship secure, production-ready CI/CD and AI-integrated features end-to-end. Design, implement, and iterate on products (e.g., hardened GitHub Actions, agent features), work closely with Product and front-end teams, make architectural decisions, and extend Chainguard's agentic AI capabilities into customer-facing experiences.

Design and implement user-facing features from concept through implementation, combining UX design and front-end engineering. Build, iterate, and maintain UI/UX and business logic to drive engagement and conversion, collaborate with product/marketing/support, participate in code reviews, and leverage modern tools and deployment workflows.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.

The role involves providing legal guidance on global privacy and AI issues, collaborating with teams to ensure compliance, and developing scalable processes.

The AI Applied Scientist Intern will tackle complex problems using deep learning, statistical analysis, and large datasets, collaborating with a team to implement AI solutions that generate economic impact.

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 applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

Partner with government customers and partners to design, prototype, and deploy AI solutions (computer vision, LLMs, generative/agentic AI). Create technical collateral, lead hands-on trials and deployments using NVIDIA hardware/software, and advocate product requirements across product, sales, and engineering teams.

Own product areas within BJAK's AI Neobank app (insurance, payments, savings, investing, travel). Define user problems, scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial products, run experiments, monitor feature performance, and iterate for improved user and business outcomes.

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.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

Build and scale Jimdo Companion: design agent orchestration, LLM-powered workflows, recommendation engines, APIs and full-stack features; enable AI platform capabilities, collaborate cross-functionally, and drive product-quality AI in production.

Design, build and ship native Android features for an AI-enabled neobank using Kotlin. Implement secure, performant user flows for payments, insurance and financial services, integrate REST APIs, improve app stability and performance, use analytics to iterate, and incorporate on-device AI where it improves the user experience.

Build and ship native Android features in Kotlin for an AI-enabled neobank app. Design clean user flows for payments, insurance and financial services, integrate backend APIs, improve performance and stability, use analytics and on-device ML (TensorFlow Lite) where it adds value.

The AI Engineering Intern will design, test, and deploy scalable AI systems, integrate models, and work on inference optimization, collaborating with data scientists on complex problems.

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