Top Machine Learning Jobs in Germany

Lead the architecture, development, deployment, and operation of production-grade machine learning systems for product recommendations. Build pipelines for candidate generation, ranking, personalization, evaluation, monitoring, and continuous improvement. Translate ambiguous product needs into scalable solutions while balancing model quality, latency, reliability, and maintainability. Provide technical leadership through architectural decisions, mentoring, design reviews, and engineering standards, while collaborating with data, product, engineering, and business stakeholders.

Lead the architecture, development, deployment, and operation of production-grade machine learning systems for recommendations, personalization, ranking, and product discovery. Translate ambiguous business needs into scalable solutions, oversee the full ML lifecycle, improve reliability and observability, and guide technical decisions. Provide technical leadership through mentoring, design reviews, engineering standards, and collaboration with product, engineering, data, AI, and business stakeholders.

Lead the architecture and technical direction of production recommendation systems. Design, develop, deploy, monitor, and continuously improve ML models for candidate generation, ranking, personalization, and product discovery. Translate ambiguous business needs into scalable solutions while balancing quality, latency, reliability, and maintainability. Provide technical leadership through design reviews, mentoring, standards, and cross-functional collaboration.

Own end-to-end hardware development projects from concept through series-production release. Coordinate hardware, software, data science, suppliers, production partners, and Sales Engineering. Manage integration milestones, outsourced work packages, deployments, budgets, risks, quality, and leadership reporting while ensuring customer requirements become delivered systems.

Develop computational and AI-based methods for de novo protein and antibody design in a biopharma setting. Responsibilities include creating and fine-tuning generative machine learning models, evolving protein design workflows, designing therapeutic variants, managing biological datasets and pipelines, and collaborating with experimental teams to validate computationally designed proteins. The role requires expertise in computational structural biology, protein folding, generative AI, protein engineering, and tools such as RFdiffusion, ProteinMPNN, Rosetta, and PyRosetta.

Develop scalable forecasting and planning software for Zalando’s logistics network. Build maintainable Python systems that optimize capacity allocation, support warehouse and transport operations, and manage the full development lifecycle from design through maintenance. Collaborate with subject matter experts and cross-functional teams to resolve operational challenges, gather requirements, and deliver milestones. The role also involves machine learning, SQL, data warehouses, ETL, pipeline orchestration, Databricks, testing, documentation, and agentic development practices.

Develops new business and revenue across DACH banking and financial-services accounts. Owns strategic account plans, complex enterprise sales, pipeline forecasting, deal closure, customer and partner relationships, and executive-level engagement. Collaborates with business development, sales engineering, account management, and leadership teams to drive adoption, customer success, and revenue growth. Represents the company at relevant events and supports go-to-market initiatives. Frequent domestic and infrequent international travel is required.

Develop and improve autonomous vehicle planner software that determines driving maneuvers. Monitor software releases, evaluate performance improvements or degradation during daily real-vehicle testing, and participate in test drives. The role requires close collaboration with testing activities and hands-on problem solving in robotics and autonomous driving.

Designs, develops, deploys, and operationalizes AI and machine learning systems for automotive engineering and business operations. The role involves architecting scalable solutions, delivering generative AI and LLM applications, optimizing model performance, implementing MLOps and governance practices, collaborating across technical and business teams, and mentoring junior engineers. The position also requires leading experimentation, validation, system integration, and continuous improvement of production-grade AI solutions.

Leads machine learning research and engineering teams focused on AI safety and the Scientist AI approach. Responsibilities include managing and mentoring researchers and engineers, hiring and onboarding technical talent, directing complex cross-functional projects, optimizing reproducible research workflows, advancing foundation-model research, and translating scientific concepts into large-scale engineering execution.

Conduct foundational AI safety research with Yoshua Bengio and the LawZero team. Develop theoretical frameworks, safe AI architectures, and probabilistic safety guarantees under finite data and compute constraints. Analyze existing research, formalize safety requirements, guide empirical experiments, publish scientific work, and communicate complex ideas to technical and non-technical audiences. The role focuses on building Scientist AI systems that are capable, transparent, and safe by design.

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.

Designs end-to-end AI transformations for complex enterprise customers. Advises HR, Finance, and IT executives; leads requirements discovery, process design, configuration guidance, data readiness, governance, and adoption workstreams. Partners with Product, Engineering, and technical teams to align AI agents, data models, security, and integrations. The role requires enterprise consulting experience, strong business acumen, AI solution design expertise, executive communication, and travel of up to 50%.

Supports AI and data consulting projects through market sizing, segmentation, pricing, optimization, predictive modeling, KPI development, dashboards, workshops, and executive presentations. The intern analyzes large datasets, develops practical client solutions, communicates complex findings to nontechnical audiences, and contributes to cross-functional strategy teams.

Own the DACH market launch for Nebuly, generating pipeline through an established enterprise network and managing the full B2B SaaS sales cycle. Target mid-market and enterprise accounts with €100K+ ACV potential, navigate complex stakeholder groups, build business cases, negotiate, and close deals. Represent Nebuly at market events, collaborate with sales engineering and partners, and develop system integrator and advisory relationships. The role requires German and English fluency, strong enterprise relationships, technical sales ability, and experience closing large contracts without inbound or SDR support.

Lead NVIDIA’s developer relations strategy across EMEA Smart Spaces and Local Government ISV ecosystems. Build partner relationships, deliver technical enablement, support integrations, guide startups, and drive adoption of NVIDIA AI and computing platforms. Collaborate with sales, solution architecture, engineering, product, and marketing teams; translate partner needs into product feedback; demonstrate AI solutions; and influence technical and commercial growth. The role requires deep AI/ML and generative AI expertise, technical collaboration skills, and experience scaling developer programs.

Develop and demonstrate Physical AI solutions on NVIDIA and hyperscaler platforms. Advise customers and partners, optimize GPU-accelerated training and inference pipelines, conduct proof-of-concepts, and collaborate with Engineering, Product, Sales, and Marketing teams. Build technical relationships, gather customer feedback, support enterprise architecture integration, and evangelize accelerated computing and generative AI to developers, researchers, executives, and technical leaders.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

Drive technical pre-sales success for large and strategic fleet opportunities. Lead discovery sessions, technical demonstrations, pilots, proof-of-concepts, solution design, customer engagements, and partner projects. Collaborate with sales, product, channel, and customer teams to address business challenges, increase revenue, and support successful implementations. Develop segment expertise, playbooks, automation, and product feedback while mentoring Solutions Engineers and ensuring opportunity tracking and customer transition processes.

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.

Build and operate production machine learning models and optimization algorithms for demand forecasting, shift planning, and staffing optimization. Develop scalable MLOps infrastructure, deployment foundations, data pipelines, monitoring, and logging. Partner with Workforce Management teams to turn operational challenges into data-driven solutions while automating and improving the data science technology stack.

Develop and ship machine learning products for Fin’s AI customer agent. Responsibilities include identifying valuable ML opportunities, framing product problems, conducting exploratory data analysis and research, selecting algorithms, performing offline evaluations, collaborating with Product, Design, and Engineering, and measuring customer impact. The role requires applied ML expertise, statistical reasoning, programming, scientific thinking, and strong cross-functional communication.

Leads NVIDIA’s developer relations strategy for manufacturing and industrial ecosystems across EMEA. Advises developers, software vendors, startups, OEMs, and integrators on AI and accelerated-computing solutions, including robotics, digital twins, simulation, computer vision, and predictive maintenance. Creates technical enablement assets, supports onboarding and integration, analyzes ecosystem opportunities, gathers developer feedback, and collaborates with engineering, product, marketing, sales, and partners to drive adoption and production-ready implementations.

Build and productionize AI-powered customer support systems, partnering with ML scientists, product, and design teams. Develop prototypes, scalable ML products, and software across Python ML systems and Rails applications. Contribute throughout the development lifecycle, measure customer impact, improve reliability and technical standards, and mentor and hire engineers.

Leads the Data Tribe, mentors team leads, and drives data-enabled business value across the company. Establishes data management, governance, integrity, and engineering guidelines; consults on data-driven practices; manages data infrastructure and analytics; supports ICT operations; and serves as Data Protection Coordinator for Engineering.