Top Machine Learning Jobs in Berlin

Own and deliver data products that power an adaptive digital shelf: define roadmap, partner with data science and engineering to build ML-driven personalization and recommendation models, run A/B experiments, monitor model and business KPIs, and align cross-functional stakeholders to drive conversion, AOV, and retention.

Lead and manage a global fraud operations team to detect and mitigate fraud across customers, riders, and vendors. Coordinate with regional, product, data, and tech teams to define priorities and ML-enabled roadmaps, build and monitor fraud KPIs, conduct qualitative risk analyses for new features, and train and mentor analysts.

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.

Develop and productionize risk and causal-inference ML models for fraud and credit, handling full ML lifecycle: data analysis, model development, deployment, monitoring, and scalable pipelines while collaborating across teams to drive business impact.

Lead go-to-market planning for JetBrains AI Ecosystem launches, create clear messaging and launch assets, coordinate stakeholders, scale campaigns, gather market and customer insights, and track campaign performance to improve processes.

Lead the ML product roadmap for speech and NLP, convert customer feedback into prioritized ML work, define model evaluation gating, partner with engineering leads to ship production improvements, and own ML product KPIs to drive measurable user impact.

Lead the Applied Science team to build frameworks for marketing effectiveness focused on causal inference and ROI to inform investment decisions across branding campaigns.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

The role involves leading research in bidding algorithms, optimizing marketplace dynamics, and mentoring a team, requiring a PhD and over 10 years of experience in machine learning and ad tech.

Support sales by conducting technical discovery, delivering customized demos and POCs, designing solutions with customers, responding to RFIs/RFPs, coordinating internal teams, answering security questionnaires, and acting as a trusted advisor on fraud/AML and financial crime prevention.

The Senior Software Engineer will develop AI-powered recruiting products using Python, manage microservices, build RESTful APIs, and collaborate with teams on scaling applications.

Design and develop AI-driven features to enhance customer experience, execute ML projects, and optimize AI performance while collaborating cross-functionally.

As Product Manager for Electronic Warfare, lead product work across capabilities, engaging with users to define requirements and manage the product lifecycle in complex environments.

Lead neuroscience R&D for EEG-based BCI: develop signal-processing and classification algorithms, design/execute human-subject experiments, integrate neural data with hardware/software, validate and ensure regulatory and ethical compliance, and mentor junior researchers.

The Head of Education & Community at Siena is responsible for developing product education, onboarding programs, community events, and market education resources. This role involves creating curricula, conducting workshops, and supporting sales with training and demos while leveraging AI for teaching efficiency.

Lead Backend Engineer responsible for building scalable, low-latency APIs and data-driven systems for Search, Recommendations, and Ranking. Design and optimize search/recommendation algorithms (including NLP), manage large data volumes with BigQuery/MPP warehouses, collaborate with data scientists, and drive architecture and feature delivery using Node.js/TypeScript.

The role involves developing new AI model architectures and adapting existing models, conducting experiments, and contributing to technical strategy in a high-growth research environment.

Lead development and rollout of global anti-fraud products balancing fraud resilience, user experience, and marketplace economics. Use data, ML/AI, and distributed systems to inform roadmap and prioritize features. Conduct analysis, support KPIs and product performance, partner with tech and global stakeholders, and localize global benchmarks to market needs.

The role involves integrating AI tools into .NET and C++ applications, optimizing models for performance, and collaborating with engineers on deployment and monitoring.

The Global Campaign of Learning Specialist will enhance operational effectiveness for USAFRICOM by integrating best practices, advanced technologies, and training programs. Responsibilities include real-time observation capture and analysis to improve decision-making.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

The Applied Machine Learning Scientist will innovate and implement ML algorithms to enhance advertising performance, collaborate with engineers, and prototype in data-driven models.