Top Machine Learning Jobs in Germany

Own the Smart Risk Engine for a B2B mobility and payments platform. Develop statistical, machine learning, and AI models; build cloud-based end-to-end ML pipelines; manage training, calibration, backtesting, monitoring, drift detection, and explainability. Create credit-risk use cases including early-warning systems, loss databases, exposure steering, and portfolio insights. Partner with senior stakeholders, translate analytics into business recommendations, and mentor Data Scientists. German and English fluency are required.

Own the end-to-end architecture for an EMAT flow sensor spanning hardware, embedded software, AI, signal processing, and cloud applications. Guide cross-functional development teams, translate product and customer requirements into technical specifications, ensure regulatory compliance, and lead system integration and testing. Serve as a technical contact for customers and stakeholders while supporting agile collaboration across international teams.

Develop and optimize machine learning models for understanding and generating 3D CAD parts. Build CAD data pipelines, implement geometric deep learning workflows, deploy models to production, improve performance, and collaborate with product and engineering teams. The role requires Python, PyTorch or TensorFlow, 3D data representation expertise, ML engineering practices, and experience productionizing models. CAD software, computational geometry, and MLOps experience are preferred.

Lead GTM operations across EMEA and APAC by developing commercial growth strategies, forecasting frameworks, territory plans, sales methodologies, dashboards, and pipeline management processes. Partner with executive leaders across Sales, Finance, Marketing, Customer Success, and Partnerships. Use Salesforce, Outreach, Snowflake, AI, and machine learning to improve forecast accuracy, pipeline conversion, reporting automation, and sales productivity.

Lead technical discovery, solution design, demonstrations, and proof-of-concept support for Anaconda’s AI and data science platform across EMEA. Partner with account executives on complex deals, create reusable technical content, represent customer needs to product and engineering, and coordinate with cross-functional teams through implementation handoff. Manage multiple opportunities independently while improving technical win rates, deal expansion, sales velocity, and regional pipeline development.

Architect and implement agentic AI solutions for semiconductor applications, including multi-agent systems, reasoning, planning, and reinforcement learning workflows. Collaborate with hardware, software, and data teams to deploy scalable, usable AI applications. Evaluate emerging agent-based AI technologies and apply them to semiconductor manufacturing challenges. The role also involves AI/ML development, web front-end integration, cloud platforms, MLOps, and continuous adaptation to evolving technologies.

Manage and expand enterprise accounts across the EMEA region through consultative selling, strategic account planning, renewals, customer retention, and new business development. Build relationships with senior executives, sell complex software and analytics solutions, maintain CRM records, monitor market trends, and collaborate with Customer Success, Marketing, SDRs, and Product teams. German and English fluency are required, with travel to events and customer meetings.

Own university and early-career recruiting for the Belgrade site, including school strategy, campus partnerships, sourcing, screening, interview coordination, offers, and closing. Build intern, co-op, and new-grad pipelines; partner with engineering managers to define candidate profiles and hiring standards; maintain ATS discipline; analyze funnel data; and improve recruiting processes and candidate experience.

Designs, builds, operationalizes, and maintains enterprise-scale data applications and scalable ETL/ELT workflows. Develops systems for collecting, ingesting, storing, processing, modeling, warehousing, and retrieving structured and unstructured data. Integrates Generative AI, LLM-based workflows, and agentic automation into secure mission systems supporting Intelligence Community objectives. Collaborates with data scientists, analysts, software developers, and stakeholders in Agile environments.

Design and implement automations and scalable workflows; work with data and automation tools; build autonomous agent systems for pricing, market analysis and forecasting; manage projects end-to-end; design data architectures and maintain clear documentation to deliver measurable business impact.

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.

Lead the strategy, design, deployment, and scale-up of AI-powered health products across the Global South. Own product roadmaps, execution, government-system integration, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, machine learning, program, and domain teams to deliver sustainable, measurable public-health impact.

Develop and maintain advanced inventory and supply planning models, inventory optimization targets, scenario analyses, dashboards, and KPIs. Use Python, SQL, BI tools, predictive modeling, and machine learning to support supply planning, S&OE/S&OP, demand-supply coverage, capacity constraints, and inventory health. Partner with planning teams to improve data quality, automation, governance, model reliability, and executive decision-making.

Design, train, and evaluate large multimodal and vision-language-action models for robotic agents. Develop reinforcement and imitation learning methods, embodied datasets, simulation environments, world models, planning systems, and sim-to-real experiments. Build robust research software and distributed training infrastructure, validate approaches on physical robots, and collaborate with research and engineering teams to deploy reliable systems. Communicate findings through technical reports, open-source releases, demonstrations, and publications.

Leads Zendesk’s EMEA Sales Engineering organization, driving enterprise market expansion, technical win rates, proof-of-concept standards, and customer adoption. Recruits and develops sales engineering leaders, partners with regional sales executives, and serves as a technical evangelist for AI-powered customer experience solutions. The role requires executive presence, public speaking, enterprise architecture expertise, cloud and integration knowledge, and strong understanding of generative AI, LLMs, and CX technologies.

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 product strategy and roadmap for Search and Discovery, building AI/ML-driven relevance, intent understanding, ranking, personalization, and experimentation. Partner with Engineering, Data Science, Design, and Commercial teams to deploy and optimize ML models, run A/B tests, and translate customer insights into measurable product outcomes. Communicate vision to stakeholders and embed search capabilities across the customer journey.

Develop and own production-ready machine learning, LLM, NLP, and RAG solutions within Palantir Foundry and AIP. Responsibilities include data exploration, model development, evaluation, deployment, monitoring, workflow integration, and optimization for accuracy, scalability, reliability, latency, and cost. Collaborate with engineers and business stakeholders, make architecture decisions, translate business needs into AI solutions, and communicate model behavior and limitations.

As an ML Application Engineer, you'll analyze engineering data, develop ML solutions, and train customer teams for AI adoption, working closely with engineering clients to enhance product design workflows using advanced technologies.

As a Senior Forward Deployed Engineer, you'll deliver AI solutions, engage with customers, and lead project lifecycles, including deployment and integration.

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.

Design and develop image/video diffusion models, perform large-scale model training, maintain data for training, and optimize models for production use.

Lead the design and delivery of a greenfield Anti Financial Crime platform: define product roadmap and architecture, translate AML/AFC requirements into technical specs, partner with AML/Ops and engineering to build detection/prevention tooling, integrate controls across products, and drive agile, high-velocity releases with strong stakeholder alignment.

Senior AI/ML Consultant focuses on transforming AI ambitions into production-ready systems, guiding clients through the AI integration process and building operational AI strategies. The role includes leading projects, mentoring junior staff, and contributing to thought leadership in Data & AI.

Develop and optimize forecasting, pricing, and decision models using time-series and heterogeneous data. Build leakage-aware features, evaluate models through backtesting, metrics, segmentation, holdouts, and A/B testing, and improve reproducible ML engineering workflows. Enhance KPI dashboards, automate analyses, and collaborate with Product and Engineering to deliver measurable product and revenue impact.