Top Machine Learning Jobs in Berlin

Leads analytical methodology and evaluation for AI-based healthcare and pharmaceutical workflows. Owns model selection, cohort and HCP segmentation, agent decision logic, and evaluation frameworks. Partners with engineering, data architecture, and governance teams to productionize rigorous analytical methods, identify new data and techniques, communicate insights, and mentor data science and engineering colleagues.

Leads ad hoc and exploratory analyses for medical and business questions across a global Medical Insights organization. Prepares and validates datasets, translates ambiguous stakeholder needs into decision-ready findings, and maintains analytical documentation. Tests new data sources, methods, and AI-enabled approaches through proof-of-concept analyses to guide platform investments and roadmap decisions. Partners with business and product stakeholders while supporting compliant, reproducible use of regulated data.

Leads data science methodology and advanced analytics for AI-enabled Medical Affairs workflows. Develops and evaluates statistical, machine-learning, NLP, and AI models using healthcare and real-world data; defines validation, monitoring, and decision frameworks; partners with engineering, governance, and business teams to operationalize solutions. Provides technical leadership, mentorship, documentation standards, and strategic guidance while ensuring privacy, compliance, responsible AI use, and clear communication of analytical findings.

Leads a strategically important Lounge by Zalando customer or business mission across Product, Engineering, Design, Analytics, and Applied Science. Owns multi-year strategy, measurable outcomes, prioritization, technical and data direction, cross-functional execution, experimentation, and continuous improvement. Builds and coaches high-performing teams, contributes to the technology leadership team, aligns with group organizations, and drives scalable capabilities, innovation, and competitive advantage through evidence-based decision-making and emerging technologies.

Develop and deploy spatial perception models for ADAS/AD, including BEV and occupancy-based architectures. Lead large-scale distributed training, synthetic data validation, model quantization, compression, and edge optimization for automotive SoCs. Translate research into production pipelines, benchmark accuracy and performance, conduct experiments, and build regression testing. Collaborate with research and engineering teams while mentoring junior engineers and maintaining high standards for code quality and documentation.

Lead the vision, strategy, and execution of company-wide data and machine learning products. Manage a senior cross-functional team, guide projects from business problem through production deployment, analyze large datasets, build data pipelines, establish evaluation and monitoring practices, and develop ML infrastructure. The role includes production LLM and agentic AI work, stakeholder collaboration, recruiting, and team development.

Lead Prior Labs’ SAP partnership and go-to-market motion for TabPFN, enabling SAP sellers, shaping co-sell and commercial frameworks, managing lighthouse accounts, developing industry use cases, and extending partnerships across hyperscalers, marketplaces, and systems integrators. Own joint business and pipeline plans, reporting, and build a specialist partnerships team while working with data science and ML buyers.

Own customer AI onboarding from initial execution through go-live and continuous improvement. Validate camera and input data quality, identify edge cases, maintain datasets and SOPs, track success metrics, communicate blockers, and coordinate installation teams to ensure reliable, consistent deployments. Improve onboarding workflows through documentation, checklists, templates, and tool enhancement recommendations. The role requires structured execution, strong communication, process discipline, technical systems comfort, and preferably manufacturing or process experience.

Own customer onboarding from initial execution through post-launch iteration. Validate camera and input data quality, identify edge cases, maintain datasets, document process variance, track success metrics, and communicate progress and blockers. Improve onboarding workflows through checklists, documentation, templates, and tool feedback. Coordinate with installation teams to ensure camera setups support reliable, consistent AI onboarding across stations.

Lead R&D for industrial robotic manipulators, including modeling, planning, control, perception, machine learning, simulation, and hardware integration. Develop and test robotic prototypes, planning algorithms, middleware, software architectures, pose estimation, segmentation, and autonomous grasping systems. Deploy and validate solutions using ROS-compatible hardware, sensors, robot arms, and real-world environments.

Leads AI initiatives for network detection and response and user and entity behavior analytics products. Designs and implements RAG pipelines, prompt engineering frameworks, and evaluation systems for reliable LLM applications. Collaborates with cybersecurity analysts, developers, and data scientists to convert security workflows into AI-assisted solutions. Establishes AI development, safety, evaluation, and deployment practices, while mentoring team members and supporting a strong AI engineering culture.

Manage full-cycle technical recruiting for AI, machine learning, and AI safety teams. Responsibilities include sourcing niche and passive talent, market mapping, talent-pool development, candidate engagement, stakeholder collaboration, offer negotiation, candidate experience, and recruitment-process improvement. The role requires strong knowledge of AI research recruiting, bilingual German and English communication, and the ability to operate effectively in a fast-paced startup environment.

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.

Lead a pod of data scientists (25%) while spending most time (75%) developing, validating, and deploying ML algorithms from sensor and imaging data for clinical decision support. Drive technical standards, production-quality code, cross-functional validation with engineers and clinicians, and contribute to Data Science leadership and regulatory readiness.

Provides proactive executive support to the CEO through agenda and travel management, time and priority optimization, stakeholder communication, project coordination, logistics, and problem-solving. The role involves frequent international travel, varying time-zone hours, confidential work, and ad hoc tasks. Candidates should be organized, adaptable, tech-savvy, communicative, fluent in English and German, authorized to work in Germany, and able to travel internationally on short notice.

Build scalable data warehouses, cloud infrastructure, AI workflows, and data-driven applications. Integrate data from multiple systems, ensure accessible and consistent data, evaluate AI use cases, and collaborate with analytics, product, and software engineering teams in an agile environment.

Own and expand strategic enterprise accounts across Europe, serving as executive relationship owner and driving revenue growth through account expansion plans. Coach account managers and customer success teams on strategy and complex commercial execution, improve key account processes, collaborate with product and customer operations, and report performance and pipeline to leadership. Represent the company with utility customers at conferences and events.

Leads the scientific strategy and development of real-time bidding and allocation algorithms for large-scale advertising platforms. Advances auction and mechanism design, reinforcement learning, optimization, Bayesian inference, and budget pacing while balancing advertiser performance, platform health, and user relevance. Translates business constraints into mathematical objectives, guides the technical roadmap, collaborates with product and engineering teams, and mentors scientists. Requires expertise in production machine learning, ultra-low-latency systems, distributed architectures, and high-dimensional advertising data.

Lead end-to-end ML product roadmaps and initiatives (e.g., Attachment Matcher, Transaction Categorizer). Collaborate with data scientists, engineers, and stakeholders to define, deliver, and monitor ML solutions that drive measurable business value for SMEs across Qonto's platform.

Lead end-to-end development and delivery of AI and ML products (including LLM/GenAI) for internal banking use. Align roadmaps with senior product leadership, partner with data scientists and engineers, define KPIs, ensure security and compliance, and create reusable AI toolkits for other teams to integrate capabilities safely and autonomously.

Own the strategy, development, deployment, and monitoring of production-grade Machine Learning and Generative AI products. Lead AI initiatives across operations, fraud, AML, growth, and marketing; partner with data scientists and engineers; define evaluation frameworks and business KPIs; establish AI platform standards; and ensure reliability, security, privacy, auditability, and regulatory compliance within a banking environment.

Serve as a senior pharmacovigilance domain expert bridging case-processing operations, product strategy, and AI automation. Guide safety workflow design, evaluate AI outputs, define future PV operating models, engage early-adopter customers, and create complex scenarios for platform stress testing. Collaborate with product, engineering, and executive customer teams to ensure accurate, compliant, and effective safety automation.

Lead the architecture, development, deployment, and operation of production-grade machine learning systems for product recommendations. Build pipelines for candidate generation, ranking, personalization, evaluation, monitoring, and continuous improvement. Translate ambiguous product needs into scalable solutions while balancing model quality, latency, reliability, and maintainability. Provide technical leadership through architectural decisions, mentoring, design reviews, and engineering standards, while collaborating with data, product, engineering, and business stakeholders.

Build and maintain Ergodic AI’s AI platform across back-end and front-end systems. The role involves developing with Python web frameworks and JavaScript/TypeScript, integrating with client technology stacks and workflows, applying AI, ML, and LLM concepts, and contributing to DevOps, cloud deployment, testing, and infrastructure practices. As an early-stage hire, the engineer will help shape the product and solve complex data and AI challenges.

Develop scalable forecasting and planning software for Zalando’s logistics network. Build maintainable Python systems that optimize capacity allocation, support warehouse and transport operations, and manage the full development lifecycle from design through maintenance. Collaborate with subject matter experts and cross-functional teams to resolve operational challenges, gather requirements, and deliver milestones. The role also involves machine learning, SQL, data warehouses, ETL, pipeline orchestration, Databricks, testing, documentation, and agentic development practices.