Top Machine Learning Jobs in Hamburg

Leads Pfizer’s enterprise applied AI strategy, architecture, governance, operating model, and transformation efforts. Drives portfolio-level technical decisions, reusable capabilities, standards, and measurable business outcomes. Provides hands-on architecture and code review using modern AI and agent frameworks while leading engineers, contractors, and vendors. Oversees quality, security, performance, spending, and technical direction, mentors technical leaders, and evaluates emerging technologies and industry trends.

Sell Zscaler Zero Trust Branch solutions across DACH, owning regional quota, building account-based strategies, partnering with solution engineers, engaging C-suite and technical stakeholders to close complex deals for SD-WAN, data security, and advanced network products.

Lead and manage a team of solution architects and consultants to design and deliver AI-driven digital transformation programs across Germany and Austria. Provide architectural direction for AI, data platforms, analytics, IoT, and application modernization; ensure secure-by-design and cloud security practices; coach and grow the team; and support strategic customer engagements and delivery execution.

Lead end-to-end enterprise sales in Financial Services for Cloudflight’s tech portfolio. Generate pipeline, engage executives, develop tailored solutions and proposals, close complex deals, and grow strategic accounts. Coordinate with delivery, architects, and partners (Microsoft, Databricks, AWS). Manage CRM, forecasting, and cross-sell/upsell activities across cloud, data, AI/ML, and custom software offerings.

As a C++ Engineer, you will develop machine learning features, integrate models, maintain SDKs, and collaborate across teams for software performance and enhancements.

Lead and grow the engineering organization to improve delivery predictability, quality, and cross-functional alignment. Drive adoption of AI-assisted software development, improve planning and execution, coach engineers and future leaders, and strengthen collaboration with Product, ML, Clinical, and Regulatory teams.

Lead customer onboarding projects for laboratories implementing AI-powered diagnostic software, coordinating product, ML, engineering, quality, IT, and clinical stakeholders through validation and go-live. Own project plans, risks, dependencies, and delivery dates. After launch, serve as the primary customer contact, managing support, monitoring engagement, coordinating issue resolution, and feeding customer insights into product planning. The role requires technical confidence, clinical or laboratory implementation experience, stakeholder leadership, and occasional international travel.

Develop and validate AI-based CT image formation solutions, including reconstruction, denoising, dose reduction, and spectral and photon-counting CT applications. Apply deep learning, data science, Python, C++, CNNs, and machine-learning libraries to create clinically meaningful imaging innovations. Provide technical guidance, shape technology direction, and collaborate with international interdisciplinary teams on global development programs.

Develop and validate AI-based CT image formation solutions, including reconstruction, denoising, image quality improvement, and dose reduction for conventional, spectral, and photon-counting CT. Apply deep learning and data science expertise, collaborate with interdisciplinary R&D teams, provide technical guidance, and help transfer research into clinically meaningful imaging innovations.

Lead development and execution of AI and automation roadmap for Western Europe Amazon e-commerce. Build AI-driven discovery and recommendation strategies, create automated reports and predictive analytics, drive AI tool adoption, ensure responsible AI governance, and run test-and-learn pilots to scale successful e-commerce AI use cases across Amazon markets.

Deliver operations-focused strategy consulting engagements across procurement, supply chain, manufacturing, and digital operations. Lead workstreams, perform advanced analytics and ML-driven solutions, synthesize recommendations, manage client relationships, and coach junior team members to drive sustainable performance improvements.

Lead operations-focused consulting engagements end-to-end, drive problem-solving and recommendations, manage teams, apply analytics and digital solutions, build client relationships, and support sustainable operational transformation.

Deliver client engagements in operations-focused performance transformation across procurement, supply chain, manufacturing, and digital operations. Lead major workstreams, perform rigorous analysis, synthesize recommendations, coach junior staff, collaborate with clients, and apply advanced analytics and digital solutions to improve operational performance.

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.

Lead and grow multiple small, high-output engineering teams focused on AI-powered vulnerability management. Stay hands-on with architecture and code reviews, drive hiring and career development, ensure technical excellence, and coordinate cross-team priorities to deliver customer and revenue impact.

Lead strategic and technical product initiatives across adjoe’s high-scale adtech platform. Define vision, translate business goals into technical roadmaps, coordinate cross-functional teams, make executive trade-offs, measure outcomes with KPIs, and mentor team members. Ensure product decisions align with system design, scalability, and revenue impact while collaborating with Data Science, Data Engineering, and Analytics.

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 end-to-end marketing strategy for DACH Public Sector to boost AI perception, generate high-value demand, run account-based programmes, manage events, vendors and agencies, and align campaigns with sales and policy stakeholders.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Lead the estimating product roadmap for an AI-powered construction SaaS: prioritize and ship MVPs, run rapid experiments, conduct user discovery with estimators, define success metrics, and iterate using data and user feedback.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

Lead neuroscience R&D for EEG-based BCI: develop signal-processing and classification algorithms, design/execute human-subject experiments, integrate neural data with hardware/software, validate and ensure regulatory and ethical compliance, and mentor junior researchers.

Lead Rasa’s product organization and define strategy for its enterprise conversational AI platform. Manage and develop a team of product managers, own product roadmaps and lifecycle decisions, and partner with engineering, sales, and marketing on product definition, pricing, packaging, and go-to-market. Use customer research, market analysis, competitive intelligence, and developer feedback to guide innovation and foster a strong ecosystem across cloud, on-premises, and open-source deployments.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

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.