Top Machine Learning Jobs in Germany

Deliver client engagements in operations-focused performance transformation across procurement, supply chain, manufacturing, and digital operations. Lead major workstreams, perform rigorous analysis, synthesize recommendations, coach junior staff, collaborate with clients, and apply advanced analytics and digital solutions to improve operational performance.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

The role involves sourcing and evaluating AI infrastructure founders, supporting them through early stages of investment, and representing the firm with technical expertise.

As an R&D Engineer, you will design and optimize AI pipelines, integrate LLMs into testing workflows, and analyze semiconductor data.

The Senior Software Engineer will focus on Machine Learning model development, data management, model deployment, and staying updated on NLP advancements.

Design, prototype, and deploy ML models for personalization in mobile games. Implement and evaluate algorithms (including RL and generative methods), run A/B tests, convert prototypes to production via MLOps, monitor player data, and collaborate with product, engineering, and analytics teams to improve retention and monetization.

Design, prototype, and deploy machine learning models for personalization in mobile games. Run experiments and A/B tests, convert prototypes to production-grade code using MLOps practices, monitor player data, and document results while collaborating with cross-functional teams to improve retention, revenue, and player experience.

Develop, validate, and integrate optical and multiphysics simulation tools into a laser Digital Twin. Model laser amplifier architectures, couple optical/thermal/mechanical domains, create reduced-order and surrogate models, validate against experiments, perform UQ and sensitivity analyses, and collaborate with engineers and experimental teams to inform system design and optimization.

Design, build, deploy, and maintain production ML systems and data platforms with emphasis on computer vision, model optimization, edge deployment, and MLOps. Develop APIs, internal tools, and CI/CD pipelines, ensure data security/compliance, and collaborate with platform and runtime teams to serve models in cloud and on edge hardware.

Join as the founding engineer to own architecture and ship features for an AI-driven adaptive learning platform. Build full-stack (Next.js/TypeScript/Python), implement LLM agents and RAG pipelines, iterate with schools, and shape technical strategy and hiring as an equity-holding co-founder.

Build, deploy, and maintain production GenAI/LLM/NLP capabilities for in-car infotainment. Design scalable AI systems, agentic workflows, evaluation pipelines, and cloud ML infrastructure. Improve reliability, safety, and quality via testing and guardrails, and integrate AI features into Android Automotive OS and Linux environments.

Own and ship core ML subsystems end-to-end for a proactive long-horizon AI assistant: data prep, training, evaluation, inference, debugging production failures, iterating rapidly, and mentoring other engineers while meeting latency, reliability, and safety constraints.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Seeking a Senior/Lead Machine Learning Engineer with expertise in inference optimization, model acceleration, and high-performance systems for developing real-time multimodal models.

As an ML Solution Consultant, you'll guide R&D teams in using ML tools, ensuring effective data structuring, and maximizing the value of scientific data.

Lead and mentor a team of Applied Scientists and AI/ML Engineers to design and deliver production-grade AI solutions across structured CRM and unstructured data. Drive technical strategy for agentic workflows, RAG/vector DB systems, and real-time reasoning. Partner with AI Platform on LLMOps and cloud infrastructure, act as a technical authority, and contribute as a high-impact individual contributor to ship customer-focused AI products.

Build ML prototypes and data pipelines, collaborate with researchers and product engineering to integrate models into Fusion, containerize and deploy applications, implement best practices, and document architectures to accelerate research-to-production workflows.

Develop intelligent agents for a materials discovery engine, orchestrating scientific workflows and integrating experimental design for sustainability solutions.

Design, deploy, and maintain scalable ML pipelines and CI/CD for production models. Build cloud-based ML infrastructure with IaC, containerization, and orchestration. Automate deployments, monitoring, rollbacks, and feedback loops. Integrate models with data teams, track performance, and provide technical leadership and mentorship to standardize workflows.

Design, build, and maintain end-to-end ML pipelines and GenAI capabilities (embeddings, RAG, vector DBs) on Vertex AI. Productionise models with Prefect orchestration, CI/CD, monitoring, drift detection, governance (GDPR, RBAC, explainability), feature stores, and stakeholder communication. Mentor engineers and embed reproducibility, scalability, and ethical AI practices.

Design, prototype, and productionize ML models for pricing, carrier forecasting, and recommender systems. Lead applied research and product discovery, integrate LLMs and AI agents, collaborate with Product and MLOps to deploy and monitor models, and translate operational problems into measurable ML solutions that drive margin and efficiency.

Build and deploy production-ready ML models for pricing, carrier forecasting, and recommender systems. Conduct deep data science and statistical exploration, partner with product and operations to translate problems into ML solutions, integrate LLMs and AI agents, and collaborate with the MLOps platform to productionize and monitor end-to-end projects.

Design, implement and optimize Retrieval-Augmented Generation platforms, document ingestion, embeddings and vector search. Build agentic workflows with LangGraph, integrate LLM providers and cloud services, containerize and automate infrastructure, implement testing and validation for RAG systems, and develop REST APIs and data models.

Design and scale predictive ML systems for VIS.X, taking end-to-end ownership of various high-impact ML initiatives and shaping the ML architecture.