Top Machine Learning Jobs in Berlin

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Lead and grow the Money & Risk engineering team responsible for fraud prevention, risk scoring, reconciliation, settlement, and chargebacks. Own technical direction for scalable, auditable, secure payment systems across 80+ countries. Drive architecture, engineering standards, AI-assisted development, hiring, coaching, and cross-functional delivery with Product, Data, Finance, Compliance, and Operations.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Own the Mapping & Content product domain end to end, including strategy, prioritization, economics, and outcomes. Lead efforts to match hotel rooms and content across suppliers, languages, and formats with high accuracy and coverage. Partner closely with engineering and data science teams on entity resolution, search, matching, catalog, identity, recommendation, and content-quality problems. Identify high-value failures, make trade-offs between precision and coverage, and help build Nuitee’s product organization in a fast-moving environment.

Lead research on interactive avatar models and real-time generative AI video agents. Set research direction, advance multimodal perception and reaction generation, post-train diffusion models, build evaluation frameworks, define datasets, and guide research-to-production efforts. Mentor researchers and influence technical decisions across research, data, and engineering teams.

Build and operate production-grade AI-enabled applications and agentic systems that improve business processes. Partner with stakeholders to identify high-value opportunities, redesign workflows, integrate APIs, data, and AI components, and take solutions from discovery through deployment. Ensure systems are tested, evaluated, monitored, and reliable in production while applying strong software engineering, communication, and business judgment.

Own the end-to-end lifecycle and strategy for B2B Lead to Sale SaaS products. Drive AI-powered features, optimize dealer workflows and conversion rates, define roadmaps and OKRs, and coordinate engineering, UX, data, marketing, sales, and external partners. Use agile, experimentation, analytics, and market insights to deliver scalable product improvements for automotive dealerships.

Own the strategy, roadmap, metrics, and adoption of JetBrains Context, a managed retrieval layer for AI coding agents. Partner with engineering, ML, platform, IDE, marketing, sales, and customer success teams to improve indexing, retrieval quality, integrations, reliability, cost, and latency. Define product direction, evaluate competitors, establish quality metrics, communicate with customers and developer communities, and drive enterprise adoption.

Lead applied science strategy for Search, build and manage teams of applied scientists and ML engineers, develop state-of-the-art query understanding, retrieval, ranking, and personalization models, drive ML platform and experimentation, establish model governance and evaluation, and align technical roadmap with business outcomes.

Own backend and infrastructure for AI shopping agents, including live checkout automation, large-scale product ingestion, search and ranking, deployment, monitoring, databases, observability, and on-call operations. Build reliable systems that handle changing retailer environments, real payments, and continuously evolving catalogs. Partner directly with the CTO and improve failure recovery, correctness, and scalability. Experience with AI agents, LLMs, retrieval, ranking, e-commerce, payments, or fraud infrastructure is valued.

Develop machine learning and analytics solutions using large-scale datasets. Responsibilities include feature engineering, predictive modeling, workflow optimization, customer implementations, production deployments, stakeholder collaboration, technical training, and resolving data-related challenges. The role requires Python, PySpark, machine learning pipelines, MLflow or Airflow, statistical modeling, and strong communication skills, with preferred experience in fraud detection, risk analytics, compliance, cloud environments, and model monitoring.

Lead frontier research in on-device AI inference, model routing, autonomous research pipelines, and real-world evaluations. Design and execute experiments involving speculative decoding, quantization, distillation, reinforcement learning, and other efficiency techniques. Own the research agenda, influence technical direction, document reproducible findings, and translate novel insights into practical performance improvements under device constraints.

Lead data science and credit risk strategy across Southeast Asia. Build and oversee production machine learning models for underwriting, fraud detection, risk segmentation, and real-time decisioning. Establish credit policies, monitor portfolio performance, lead stress testing, and support responsible market expansion. Build and mentor data science and risk teams while partnering with engineering, product, finance, and executives to improve approvals, defaults, time-to-decision, and unit economics.

Generate profitable public-sector revenue in Germany by selling cybersecurity solutions through channel partners. Achieve quotas, manage opportunities, support renewals, engage customers, maintain CRM and forecasting accuracy, provide quotes, and communicate Sophos’s value proposition. The role requires IT solution sales experience, channel ecosystem knowledge, negotiation and presentation skills, strong telephone sales abilities, fluent German, and legal authorization to work without employer sponsorship.

Leads Tide’s engineering organization for fraud, financial crime, and ongoing monitoring platforms. Owns engineering strategy, decisioning infrastructure, model lifecycle, agentic AI deployment, and global platform scalability. Partners with risk, compliance, operations, product, and data science leaders to improve fraud detection, reduce losses and monitoring costs, automate workflows, and meet regulatory requirements across multiple markets.

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.

The role involves enhancing operational effectiveness in military operations through exercise development, collaboration, and innovation methodologies, focusing on integrating advanced technologies.

Leads strategic R&D engagements from presales through delivery, translating scientific and business challenges into AI architectures, product backlogs, MVPs, simulations, integrations, data pipelines, and production solutions. Builds trusted relationships with R&D executives, scientists, engineers, and technical teams; assesses data, compute, feasibility, value, staffing, and delivery plans. Develops reusable architectures and playbooks, oversees technical workstreams, evaluates emerging AI capabilities, and provides feedback to shape Microsoft Discovery products and scalable industry solutions.

Owns full-cycle recruiting for commercial, operational, and technical roles across global markets. Responsibilities include sourcing passive candidates, building pipelines for hard-to-fill positions, advising hiring leaders, coordinating interviews and offers, maintaining accurate Greenhouse data, and improving hiring processes through systems thinking, data, AI, and automation.

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.

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.

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.