Top Machine Learning Jobs in Germany

Design, build, and operationalize scalable AI/ML applications and data pipelines. Retrieve, parse, and process structured and unstructured data to support model training and deployment. Code in Python or TypeScript, collaborate with cross-functional teams, and maintain enterprise-scale ML systems. Support monitoring, scaling, and operation of AI/ML platforms while working on mission-driven projects requiring a Top Secret clearance.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

Create and review realistic, locale-specific robotics and natural sciences tasks in the target language, apply scoring rubrics, evaluate AI and human responses for accuracy and regulatory alignment, and produce high-quality gold-standard solutions and feedback.

As the Senior Expert/VP for Reinforcement Learning, lead the development of AI testing frameworks and validate RL systems in complex environments.

As an Engagement Manager, you'll ensure customer success in protein R&D by building relationships, tracking metrics, and leading cross-functional teams. You'll manage enterprise accounts and advocate for customers while aligning their needs with Cradle's offerings.

Lead development and productionization of TTS and voice-generation systems: train, fine-tune, evaluate models, deploy and optimize for inference, latency, scaling, monitoring, and reliability; translate research into production and define technical direction while collaborating with research, engineering, and product.

Lead legal counsel for an adtech company providing commercial, product, privacy, and regulatory counsel. Negotiate complex agreements, advise on global privacy and IAB standards, support product launches and AI use cases, manage data governance and incident response, oversee corporate compliance and outside counsel, and build scalable legal operations and contract 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.

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.

Ship AI product features combining engineering and ML; build and improve agent workflows; optimize latency, cost, and reliability; track AI research and turn it into product; build self-improving loops that run experiments and learn from feedback.

Support ~25–30 learners in a virtual ML/data program by answering questions, facilitating breakout rooms, holding office hours, grading submissions, monitoring Slack, reviewing materials, and providing constructive feedback. Provide bilingual (German/English) technical support focused on data annotation, labeling QA, and generative AI concepts. Participate in onboarding, weekly staff meetings, and submit program improvement write-ups.

Lead end-to-end architecture and integration of battery insights algorithms into production embedded systems. Define modular architectures, drive algorithm development (signal processing, ML, modeling), translate algorithms to real-time software, guide cross-functional teams, mitigate risks, and mentor engineers to ensure scalable, maintainable, production-ready BMS solutions.

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

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

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.

Constructor University offers Ph.D. positions for MSc graduates in Computer Science focusing on AI and ML, including research and an industrial internship.

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.

Design and develop control architectures and advanced control algorithms for integrated electrical and automation systems. Lead subsystem integration for synchronized plant operation, modernize power management strategies, and implement diagnostics and predictive maintenance to improve reliability and performance.

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.

Lead research and development of AI accelerator/NPU IPs for embedded products. Architect RTL and hardware to meet power/performance/area targets, collaborate with compiler and software teams, model systems, and mentor cross-functional teams toward chip tape-out.

The role involves building and maintaining ML and cloud infrastructure for training large-scale generative models. Responsibilities include designing GPU clusters, supporting distributed training, managing storage systems, and collaborating with researchers and engineers.

Design, implement, and maintain MLOps/LLMOps pipelines and infrastructure. Build CI/CD for ML/LLM deployments, optimize cost/latency/scalability, automate retraining, manage cloud/on-prem resources, use container orchestration, implement data versioning and experiment tracking, document practices, and mentor team members.

Design, build, and operate production-grade ML systems for retail media (ranking, segmentation, optimization, attribution). Own end-to-end ML lifecycle: data, feature engineering, training, deployment, monitoring, and continuous improvement while collaborating with product, engineering, and business stakeholders.

Design, build and operate scalable ML infrastructure and pipelines on AWS to deploy, monitor and retrain models for personalization, recommendations, search and ranking. Collaborate with data scientists, engineers and product teams to ensure availability, observability and continuous improvement of production ML systems.

Provide mobility planning and execution support for AFRICOM exercises, including deployment planning, multimodal movement analysis, theater distribution, risk assessments, and integration of AI/ML logistics tools. Advise planners on funding, contracts, cargo prioritization, and produce mobility annexes, TPFDD inputs, and senior leader briefings. Participate in planning conferences, site surveys, and execution events to synchronize mobility operations.