Top Machine Learning Jobs in Germany

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

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.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

Build foundational MLOps infrastructure and deploy/scale heavy-width CV and VLM models for luxury product authentication and fraud detection. Design data/feature management, model registry, serving, monitoring, and automated retraining pipelines. Evaluate tooling vs enterprise platforms, ensure low-latency high-throughput inference, and centralize ML engineering support across search, recommendations, pricing, and marketing while mentoring teams and enforcing engineering hygiene.

Lead and grow a ~50-person ML organization, prioritize high-impact AI projects, set ML technical direction and standards, ensure delivery and measurable business value, and collaborate across products to translate AI research into product innovations.

The AI/ML Engineer will develop and optimize machine learning models, work with cross-functional teams, and enhance mobile app features.

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.

As an AI Research Engineer at Helsing, you'll architect and enhance deep learning frameworks, optimize infrastructure for distributed training, and develop strategies for large datasets while collaborating with a cross-functional team.

As an AI Research Engineer, you will develop signal processing solutions, collaborate across teams, and apply AI in real-world scenarios within defence.

Lead pre-sales technical discovery and serve as the primary architectural authority for enterprise AWS AI/ML, data, and cloud modernization initiatives. Design Bedrock and SageMaker solutions, migration strategies, and modern data platforms; develop AWS cost models, SOWs, proposals, and funding inputs; evaluate risks and compliance requirements; and coordinate technical handoffs with delivery teams. The role partners with account executives, AWS field teams, customers, and internal engineers without carrying a sales quota.

Build and operate production machine-learning systems for financial-crime transaction monitoring. Responsibilities include developing batch training pipelines, engineering AML and fraud features, deploying and monitoring models, evaluating drift and performance, calibrating thresholds, conducting sensitivity tests, and producing governance documentation. The role partners with AML investigators, fraud operations, product, and engineering teams to deliver explainable, auditable, and scalable ML solutions.

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.

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.

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.

Leads development and deployment of expert and generalist robot policies for industrial automation. Designs task-planning and policy-learning systems using hierarchical planning, reinforcement learning, and hybrid methods. Develops simulation and real-world datasets, guides sensor integration, collaborates across embedded and hardware teams, and advances AI platform architecture, CI/CD, and MLOps. Provides technical leadership, establishes roadmaps, mentors engineers, and deploys robotics algorithms into real-world systems.

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.

As a Senior Machine Learning Engineer, you will develop and scale customer-facing AI models, ensuring quality integration with business solutions, and mentoring peers.

The role involves building and operating ML systems and infrastructure for large foundation models, data pipelines, and experiment orchestration in a collaborative environment.

Design, develop, and deploy end-to-end deep learning driving models focused on performance, robustness, personalization, and collaboration. Build data pipelines, evaluation frameworks, and deployment pathways; mine real and synthetic data; influence architecture and training; collaborate cross-functionally and mentor engineers for production-scale autonomy.

Lead development and evolution of end-to-end ML pipelines, focusing on integration, orchestration, automation, reproducibility, and model serving to improve forecasting and dynamic pricing. Shape ML architecture, drive data-driven decisions, and collaborate across multidisciplinary, international teams.

As an Intern Machine Learning, you will support the integration of AI solutions in healthcare, collaborating with teams to build and deploy machine learning models in production environments.

Build and own end-to-end frontend and backend ML platform features, collaborate with product and research to productionize model and agent capabilities, and design scalable, reliable, and secure data science tools.

As a Senior AI Engineer, you will develop AI agents, integrate features into a web app, enhance matching systems, and collaborate across teams to deliver ML solutions.

Lead end-to-end face ML projects: define data and labels, train and optimize deep-learning and classical CV models under latency/memory limits, build evaluation and monitoring pipelines, investigate failures, and ship production-quality solutions while collaborating across mobile, infra, and product teams.