Top Artificial Intelligence Jobs in Hamburg

Lead engineering execution and technical direction while remaining approximately 75% hands-on. Responsibilities include developing LLM- and AI-powered products, coding, code review, debugging, platform performance, sprint planning, cross-functional coordination, technical mentorship, recruiting, and supporting engineers’ growth. The role partners closely with founders and product teams and offers a path toward Engineering Manager responsibilities.

Own AI Investing product areas from user research and problem definition through roadmap planning, launch, measurement, and iteration. Partner with engineering, design, data, operations, compliance, and business teams to simplify financial products, make informed trade-offs, and deliver customer-focused features quickly. Use metrics, feedback, experiments, and production insights to improve outcomes across insurance, payments, savings, investing, travel, and related financial services.

Leads global commercial customer data governance, Customer 360 strategy, segmentation, data quality, reporting, and insight initiatives. Owns customer definitions and hierarchies across SAP and Salesforce, establishes governance and KPI standards, and aligns reporting and analytics investments with business value. Partners cross-functionally to improve data quality, reporting adoption, forecast accuracy, and AI readiness while driving change management and value realization across the commercial organization.

Lead AI transformation across Sales, Marketing, Customer Success, Partnerships, and Revenue Operations. Identify high-value opportunities, design and deploy production AI agents, agentic workflows, automations, integrations, and enterprise solutions, then drive adoption and measure business impact. Partner with functional leaders, make sound decisions about AI suitability, redesign operating models, improve reliability and governance, and mentor colleagues while establishing reusable standards across the organization.

Own product marketing for Cyara’s Agentic AI assurance products, including positioning, messaging, go-to-market launches, competitive intelligence, sales enablement, technical content, and cross-functional collaboration. The role targets enterprise buyers through sales-led motions and requires measurable pipeline impact, strong writing and presentation skills, and comfort working with technical teams. Experience in AI, contact centers, QA, testing, observability, or monitoring is advantageous.

Lead product direction and execution for major areas of BJAK's AI Neobank app. Define roadmap, priorities, standards and success metrics across multiple squads; guide PMs, designers and engineers; translate regulatory and operational constraints into product decisions; drive launch quality, post-launch learning and continuous improvement; work across insurance, payments, savings, investing and travel.

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.

Build AI-powered features, LLM integrations, and multi-step agent workflows for requirements and systems engineering software. Integrate agents with internal data sources, reason over requirements documents and system specifications, address security and data privacy, and deploy AI capabilities into production. The role requires software engineering experience, hands-on LLM integration, end-to-end ownership, and collaboration in a startup environment.

Own end-to-end infrastructure delivery for strategic physical AI customers, from discovery and architecture through production rollout. Build cloud compute orchestration, platform services, onboarding environments, observability, security, and cost controls for simulation, training, inference, and batch workloads. Debug across infrastructure layers, productize recurring customer needs, and shape the physical AI platform roadmap. Partner with customers, Field CTO, Product, and Engineering while using AI coding tools to accelerate production engineering.

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.

Lead global marketing and corporate communications to define Multiverse's narrative, positioning, and GTM for AI/quantum products. Partner with the CEO, build and scale the marketing & communications team, own product messaging, PR, executive positioning, agency relationships, and measure impact on awareness and enterprise pipeline.

Supports Account Managers and client teams by coordinating delegated tasks, tracking deadlines and follow-ups, preparing updates, managing workflows, communicating in German and English, reviewing AI-generated work, and improving repeatable account processes. The role requires strong client judgment, attention to detail, prioritization, technical adaptability, and hands-on use of AI tools and agents in a remote, async environment.

Join Welo Data’s global contributor network for flexible, remote AI projects. Depending on skills, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and requirements provided before each assignment.

Owns commercial success, revenue, and strategic relationships for embedded HR technology partners. Responsibilities include managing a $10M+ annual bookings target, developing partner sales motions, enabling partner sellers, driving pipeline and ARR growth, resolving commercial issues, leading executive business reviews, expanding partnerships, coordinating cross-functional teams, and managing partner account managers and embedded sales leads.

Lead the company’s Physical AI research and engineering function as a hands-on technical leader. Define research priorities, design and train Transformer-based models, establish rigorous offline and real-world evaluations, integrate systems with physical robots, and analyze failures across data, models, hardware, and environments. Build a focused team, guide technical architecture and experiments, translate customer needs into requirements, and deliver measurable improvements through deployed models, benchmarks, evaluation systems, or data products.

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.

Own the technical design and production delivery of multi-component AI systems. Build evaluation frameworks for nondeterministic behavior, data pipelines, feedback loops, model-tool boundaries, and agent harnesses. Optimize latency and cost through streaming, caching, routing, and prompt design. Develop internal tooling, assess external models and datasets, support APIs and SDKs, mentor engineers, and participate in 24/7 on-call operations.

Own the AI cost visibility product end to end, developing Go backend services, APIs, data processing, and TypeScript/React interfaces. Translate AI spending data into clear FinOps experiences, work closely with customers and product needs, and use AI throughout the engineering workflow. Lead feature design, implementation, release, monitoring, and iteration while setting engineering standards, mentoring peers, and building scalable cloud-based SaaS capabilities.

Own the AI cost visibility product end-to-end, building Go backend services, APIs, and TypeScript/React interfaces. Translate cloud AI spend and usage data into clear FinOps experiences, while driving architecture, reliability, monitoring, and iteration. Collaborate closely with customers and product stakeholders, use AI throughout the engineering workflow, mentor peers, and shape product direction in an ambiguous market.

Own AI cost visibility features end-to-end, from customer problem discovery and system design through implementation, release, monitoring, and iteration. Build scalable Go backend services, APIs, and TypeScript/React interfaces that process and present cloud AI spend data. Apply AI throughout the engineering workflow, contribute to product direction, understand FinOps and AI economics, mentor peers, and uphold reliable engineering practices.

Build the end-to-end analytics foundation for a new mobile game, including gameplay tracking, data architecture, pipelines, semantic models, reporting, and AI-powered insight workflows. Use agentic engineering tools to accelerate development while owning correctness, quality gates, and production review. Analyze player behavior, monetization, attribution, anomalies, and trends to support product and leadership decisions. Establish organizational standards for AI-assisted development and independently drive technical initiatives from concept through production.

Build and operate Typeform’s internal AI Operations capability for R&D. Identify and prioritize AI opportunities, discover workflows, design and deliver AI-native solutions, and manage their lifecycle after launch. Responsibilities include improving enterprise AI tools, coordinating access and governance, evaluating reliability and security, supporting automations and agents, measuring adoption and value, and partnering with Product, Engineering, IT, InfoSec, Finance, and other teams.

Citrine Informatics invites applicants to submit resumes for general opportunities. They review applications periodically for potential new roles in AI-driven materials and chemicals.

Join Welo Data’s Canadian French contributor network for potential remote AI projects. Depending on project needs, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts for large language models; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and details provided before each assignment.

Join Welo Data’s French AI Trainers Network as a flexible contributor supporting annotation, evaluation, and prompt creation projects. Contributors may label text, images, or audio; assess search results, advertisements, and chatbot responses; create prompts for large language models; and ensure cultural and linguistic accuracy. This is a talent community rather than an active opening, with project details and compensation provided when assignments become available.