Top Machine Learning Jobs in Berlin

Research and investigate global fraud trends using BioCatch data and external sources. Produce data-driven insights, visualizations, written thought leadership, dashboards, and presentations. Build repositories and automate analysis workflows while applying fraud expertise and AI tools to validate findings and support sales and marketing.

Lead the Weaviate Database product: own roadmap and strategy, translate customer and research insights into features across schema, storage, replication, and search quality, coordinate cross-functional teams, drive developer adoption, and measure feature outcomes post-release.

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.

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

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.

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

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.

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.

Develop, design, test and implement imaging and 3D reconstruction algorithms using C++ and Python. Analyze real data, optimize for high-performance computing, integrate algorithms into applications, and collaborate across algorithms, physics, and electronics teams while participating in design and code reviews.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Lead the Recommendations engineering team to build scalable, reliable recommendation-as-a-service products. Own recommendation quality and business impact, set technical direction, translate product goals into engineering plans, ensure SLAs, hire and coach engineers, and drive system reliability and scalability.

Lead product strategy and roadmap for query interpretation and search quality. Own models, tools, and metrics to optimize recall and precision. Collaborate with ML, infra, and product teams, triage quality issues, benchmark competitors, and build evaluation frameworks and explainability for search results.

Lead research, design, and production implementation of algorithms and predictive models for warehouse scheduling, planning, and routing. Deliver end-to-end solutions from prototype to highly available production services, mentor applied scientists, and influence scalable, data-driven architecture and development methodologies.

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

Design, build, and scale core infrastructure for machine learning models, manage production systems, and implement best practices in MLOps.

Lead applied ML efforts to embed LLMs and agentic workflows into production, focusing on anti-fraud, automated evaluation, and operational automation. Design reusable architectures, implement observability and A/B tests, partner with delivery and clients, and set engineering standards for prompts, agents, pipelines, and CI/CD.

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.

Lead design, build, and production deployment of ML/LLM systems for customer support. Develop ML infrastructure, pipelines, model serving, fine-tune models, preprocess large text datasets, and collaborate with cross-functional teams to integrate AI solutions.

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 operate end-to-end data engineering and MLOps solutions for real-time, low-latency ad personalization. Implement streaming pipelines (Flink, Spark), feature provisioning to Hopsworks, model serving and operationalization (Triton, SageMaker), CI/CD, monitoring, and automation. Collaborate with applied scientists and mentor engineers while owning cloud infrastructure (AWS, Databricks, Kubernetes) and production reliability.

As a Machine Learning Scientist at Nucs AI, you will develop innovative ML methods for medical image analysis, collaborate with clinicians, and contribute to clinical validation studies, impacting cancer diagnostics.

Design, build, and maintain end-to-end scalable data pipelines and Snowflake-native warehouses; optimize SQL and performance; implement Azure-based data platform solutions; develop Power BI semantic models; enable AI/ML data workflows; enforce governance, CI/CD, and security; collaborate with stakeholders and mentor engineers.

Design, build, and maintain MLOps tools, automation, and end-to-end ML pipelines. Support large-scale GPU clusters, implement monitoring/observability, streamline reproducible training and deployment, and collaborate with product and engineering teams to optimize ML workflows for scalability and cost-efficiency.