Top Machine Learning Jobs in Germany

Design, build, and operate ML platform systems to train, evaluate, deploy, and serve generative models. Improve reliability, scalability, observability, and cost-efficiency of GPU and cloud workloads. Build internal tools and agent-friendly workflows, collaborate with researchers and engineers, and drive platform architecture and automation.

Design, prototype, and productionize ML models for pricing, carrier forecasting, and recommender systems. Conduct deep statistical analysis, partner with product and operations for discovery, integrate LLMs where appropriate, and collaborate with MLOps to deploy and monitor end-to-end solutions.

Build and operate ML infrastructure powering model training, evaluation, deployment, inference, observability, and continuous improvement. Improve reliability, scalability, latency, throughput, and cost efficiency. Provide tooling and pipelines enabling fast experimentation, reproducible ML workflows, benchmarking, monitoring, and platform primitives consumable by AI engineers and researchers.

Develop and maintain a low-level ML model execution runtime in Rust, port and optimize ML models for custom hardware, maximize performance and utilization, and debug/productize models in virtual and physical hardware environments.

Design, build and productionize scalable recommendation ML systems: develop and optimize training, deployment, and monitoring pipelines using Airflow, AWS, PySpark/Databricks and deep learning frameworks, ensuring reliable real-time and batch inference and improved data quality.

As an AI/ML Engineer, you will develop and optimize machine learning models for mobile apps, collaborating with cross-functional teams to enhance user experiences.

Design and build end-to-end ML systems for fraud detection: develop and deploy models, create production-ready features and real-time data pipelines, integrate with backend services, and ensure monitoring, security, and observability.

Design, build, and deploy AI systems for early detection of inflammation-related diseases. Lead AI infrastructure, data modeling using medical standards and ontologies, analyze data for strategic decisions, and develop long-term AI roadmaps while ensuring ethical and compliant AI practices.

Lead design and productionization of ML pipelines, evaluation systems, and AI agents for cybersecurity. Own experimentation-to-production lifecycles, integrate ML across product teams, scale ML infrastructure, optimize agent performance, and mentor junior engineers while collaborating with CTO and product leadership.

Design and implement scalable MLOps/LLMOps systems and tools to productionize ML and LLM models. Build platform capabilities (orchestration, CI/CD, versioning, monitoring, testing) and collaborate with product teams to deploy, evaluate, and operate AI-powered features at scale while driving best practices and mentoring teammates.

Design and implement scalable machine learning systems, collaborate with product teams, and apply MLOps best practices in production environments.

Design, train, fine-tune, distill, and deploy LLM-based and agentic ML models. Build and operate agent workflows in Python, run evaluation and benchmarking, select models and cost strategies, and manage production monitoring and observability for model performance and reliability.

Build and operate production-grade, low-latency ML platforms and feature-serving infrastructure. Design data-intensive streaming systems, create reusable engineering patterns, collaborate with ML/data/backend teams, and mentor peers to improve platform reliability and scalability.

Own, design, build, and operate distributed, high-load backend services and data pipelines for ML infrastructure. Partner with ML and product teams to deliver self-service, reliable model serving, storage, and deployment components; ensure observability, scalability, and performance while driving technical decisions and continuous improvement.

Build, deploy, and operate production-grade ML systems and pipelines using Python and cloud infrastructure. Implement scalable model serving (batch and real-time), containerize services, maintain CI/CD, testing, monitoring, and improve system performance, scalability, and cost-efficiency.

Build, optimize, and maintain a Rust-based real-time ML inference engine and SDK for Voice AI. Work on model execution, DSP modules, cross-platform bindings (C/C++, Python, WASM), performance profiling, testing, and tooling to ensure low-latency, reliable audio inference across platforms and runtimes.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Build and ship state-of-the-art speech systems end-to-end: model architecture, training (pre-train/fine-tune/post-train), evaluation, tooling, data curation, production inference, and safety/abuse mitigation for voice conversion and related tasks.

Lead and coordinate technical AI/ML programs bridging research and lab implementation. Plan and deliver projects, manage compute/resources/dependencies, liaise with industry and academic partners, drive proposals and pilots, refine scalable processes, track metrics, and support IP, compliance, and publication efforts.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Build and scale ML-powered search and RAG capabilities at Zendesk scale. Collaborate with product, ML scientists, and engineers to design, deploy, and maintain reliable production services, improve relevance and ranking, optimize indexing pipelines, and mentor teammates while applying experiments and analysis to drive decisions.

The role involves technical leadership in ML for pricing and revenue, including model development, experimentation, and ensuring operational reliability with a focus on business impact.

The Principal Machine Learning Architect will design and implement data-centric ML solutions, ensuring high-quality ML model development and deployment strategies for a leading ad tech company.

Own and evolve ClickHouse's Python connector and SDK ecosystem, build integrations with orchestration and transformation tools, drive AI/LLM integration strategy for RAG and feature pipelines, engage with open-source community, and collaborate across product and engineering to prioritize real-world data workflows.