Top Machine Learning Jobs in Germany

Lead the Agentic Discovery product chapter, initially owning business tooling, dashboards, transparency, and controls for AI-powered commerce experiences. Define strategy, align ML, engineering, design, and go-to-market teams, scale experimentation, launch reliable LLM-powered experiences, and establish metrics tied to usage, conversion, revenue, and engagement. The role will expand to chapter-wide leadership across on-site, offsite, and integrated agentic discovery surfaces.

Designs, builds, and evaluates research prototypes and production-ready systems for portable AI agents, model routing, provider interoperability, memory, retrieval, tool use, and multi-agent workflows. Responsibilities include developing evaluation frameworks, conducting statistically rigorous experiments, creating schemas and APIs, optimizing model and agent systems, and translating research into secure, reliable, reusable components. The role collaborates across research, infrastructure, security, product, and engineering teams and communicates findings through technical reports, benchmarks, demonstrations, open-source releases, and publications.

Build and own scalable machine learning and generative AI systems for customer-facing and operational healthcare use cases. Develop production pipelines for training, evaluation, deployment, monitoring, and continuous improvement. Collaborate with product, engineering, data, and business teams; explore emerging AI technologies; define technical roadmaps; and uphold governance, model quality, versioning, observability, and compliance practices.

Build scalable data warehouses, cloud infrastructure, AI workflows, and data-driven applications. Integrate data from multiple systems, ensure accessible and consistent data, evaluate AI use cases, and collaborate with analytics, product, and software engineering teams in an agile environment.

Own and expand strategic enterprise accounts across Europe, serving as executive relationship owner and driving revenue growth through account expansion plans. Coach account managers and customer success teams on strategy and complex commercial execution, improve key account processes, collaborate with product and customer operations, and report performance and pipeline to leadership. Represent the company with utility customers at conferences and events.

Leads consultative discovery, technical qualification, solution design, executive presentations, and sales campaigns for AspenTech industrial software. Partners with sales teams to align technology with measurable customer value, develops industry-specific messaging, engages senior executives, provides product feedback, coaches sales personnel, and represents the company at industry events. Requires extensive industrial software or solution consulting experience, technical domain expertise, multilingual fluency, and European work authorization.

Build and grow Cloudera’s sovereign cloud business across EMEA. Develop relationships with sovereign cloud providers and enterprise customers, create marketplace offerings, drive partner enablement and sales motions, generate pipeline, and manage regional forecasts and bookings. The role requires expertise in enterprise cloud, data, AI/ML, sovereignty regulations, complex multi-stakeholder deals, executive communication, and cross-functional collaboration.

Design legal AI tasks, datasets, workflows, and evaluation rubrics; assess system outputs for legal accuracy and usefulness; translate legal processes into agentic workflows; lead enterprise pilots and implementations; and support customer discovery, demonstrations, and go-to-market initiatives. The role requires close collaboration with AI researchers and engineers and offers broad ownership in an early-stage company.

Develop research methods and systems for matter-specific legal AI, including model and agent adaptation, data curation, benchmark design, training, inference, experimentation, and evaluation infrastructure. Collaborate with founders, engineers, legal experts, and enterprise partners to translate legal workflows into agentic systems and production capabilities. Help shape the company’s technical direction and research culture while owning empirical research and engineering projects end to end.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Own the full enterprise customer journey from kickoff through adoption and ongoing value realization. Build executive and technical stakeholder relationships, lead QBRs and EBRs, translate product performance into business outcomes, manage risks and escalations, provide strategic ecommerce recommendations, maintain success plans, and partner with Account Management on retention and expansion.

Own enterprise customer relationships from kickoff through adoption and ongoing value realization. Lead success plans, QBRs, executive conversations, escalations, stakeholder alignment, training, and strategic recommendations. Translate technical capabilities and performance data into business outcomes such as revenue, conversion, and customer experience. Partner with Account Management to support retention and expansion while improving scalable onboarding and support processes.

Leads the transformation of translational and biomarker data into standardized, AI-ready data products for drug discovery and development. Oversees data standards, catalogs, metadata, harmonization, quality frameworks, APIs, automation, and enterprise integration. Manages a distributed team of 12–15 data stewards and engineers, establishes multi-year strategy, coordinates cross-functional stakeholders, oversees budgets, and advances FAIR data practices, governance, and AI-enabled workflows across therapeutic areas and research partners.

Leads a distributed team transforming translational and biomarker data into standardized, FAIR-compliant, AI-ready data products. Responsibilities include defining data standards, metadata, semantic schemas, quality frameworks, APIs, and integration with enterprise data platforms. The role delivers analysis-ready datasets for precision medicine and biomarker discovery, automates curation and quality assurance, establishes multi-year data roadmaps, coordinates global stakeholders, manages budgets, and advances data governance across labs, studies, therapeutic areas, and partners.

Leads the global digital operations and product management portfolio across transportation, supply chain planning, fulfillment, and operational visibility. Owns product roadmaps, investment decisions, delivery, adoption, and continuous improvement while partnering with supply chain, engineering, data science, BI, architecture, ERP, and technology teams. Develops applied AI and machine learning use cases for operational challenges, manages an international team, and drives measurable business value across global distribution operations.

Develop and optimize modern C++ software for sensor-based industrial sorting machines. Responsibilities include implementing machine logic and software modules, troubleshooting issues, testing directly on equipment, processing sensor data, documenting solutions, and collaborating with electrical, mechanical, production, and service teams. The role also supports continuous improvements in software quality, performance, and efficiency.

Lead end-to-end ML product roadmaps and initiatives (e.g., Attachment Matcher, Transaction Categorizer). Collaborate with data scientists, engineers, and stakeholders to define, deliver, and monitor ML solutions that drive measurable business value for SMEs across Qonto's platform.

Drive business development and close high-value enterprise deals with global biotech and biopharma accounts across Europe. Own strategic customer relationships, deliver recurring revenue, partner with technical and customer success teams on complex proposals, provide market insights for product direction, build industry credibility, and improve enterprise sales processes and playbooks.

Designs production-ready AI architectures for customer experience and contact center use cases. Responsibilities include RAG and knowledge architecture, AI evaluation and observability, contextual and agentic workflows, enterprise integrations, prototypes, technical demonstrations, presales solutioning, executive engagement, field enablement, and product feedback. The role optimizes AI systems for scalability, latency, cost, reliability, governance, and business outcomes while influencing CIO and CTO stakeholders.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Lead end-to-end development and delivery of AI and ML products (including LLM/GenAI) for internal banking use. Align roadmaps with senior product leadership, partner with data scientists and engineers, define KPIs, ensure security and compliance, and create reusable AI toolkits for other teams to integrate capabilities safely and autonomously.

Own the strategy, development, deployment, and monitoring of production-grade Machine Learning and Generative AI products. Lead AI initiatives across operations, fraud, AML, growth, and marketing; partner with data scientists and engineers; define evaluation frameworks and business KPIs; establish AI platform standards; and ensure reliability, security, privacy, auditability, and regulatory compliance within a banking environment.

Serve as a senior pharmacovigilance domain expert bridging case-processing operations, product strategy, and AI automation. Guide safety workflow design, evaluate AI outputs, define future PV operating models, engage early-adopter customers, and create complex scenarios for platform stress testing. Collaborate with product, engineering, and executive customer teams to ensure accurate, compliant, and effective safety automation.

Lead the data intelligence dimension of Agileday’s AI product by building AI-ready data foundations, predictive and recommendation capabilities, learning systems, feedback loops, and production data pipelines. Partner with founders and product teams to identify commercially valuable opportunities, shape architecture and quality standards, and move solutions from experimentation into reliable customer-facing products.