Top Machine Learning Jobs in Berlin

Design and implement algorithms and probabilistic models for array and NGS genotyping. Develop production-grade bioinformatics software and scalable workflows, apply statistical and machine learning methods to large genomic datasets, and maintain testing automation, CI/CD, observability, and software lifecycle practices. Collaborate with wet‑lab scientists and engineers; provide mentorship at staff level.

Work directly with founders on end-to-end high-priority projects: run customer pilots and field deployments, assemble and install sensor/compute hardware, perform data analysis and market/unit-economics modeling, build investor/customer materials, and create internal tooling and automations across engineering, commercial, and operations.

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 technical presales engagements for an AI-driven commerce platform: run discovery sessions, design composable solution blueprints, build demos and prototypes, author technical RFP/RFI responses, and collaborate with GTM, Product, and Engineering to close enterprise deals across the DACH region.

Own post-sale customer lifecycle for Portuguese utility accounts: onboard, embed Neara into workflows (vegetation management, clearance analysis, CAPEX), run QBRs, monitor health scores, drive renewals and upsell, and align product roadmap with local market needs.

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

Own Go-To-Market for performance media buyers: build relationships, close and onboard clients, represent Kayzen at industry events, advise prospects on product strategy, and funnel market feedback across teams.

Own and scale end-to-end forecasting and price-elasticity models for the pricing engine. Build, deploy, and monitor models; design pricing experiments and A/B tests; generate commercial insights; partner with stakeholders to drive strategy; and establish analytics and experimentation best practices.

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.

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.

Own end-to-end delivery of ADAS features (AEB, LKA, ESA, AES, BSM), align OEM requirements with internal roadmaps, specify features, collaborate with engineering and product teams, support testing/validation, mitigate risks, and maintain strategic feature roadmaps toward higher autonomy.

Lead product development of Zalando's pricing platform by defining vision, prioritizing problems, and driving delivery with engineering, operations research, and data science teams. Build forecasting and optimization infrastructure to recommend prices across hundreds of thousands of items, communicate complex quantitative topics to stakeholders, and measure commercial impact.

Support multilingual data quality by analyzing and cleaning text data, developing parsers, evaluating text classifiers, translating taxonomies (Skills, Titles, Occupations), and partnering with Linguistics and Engineering to deliver high-quality global data.

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 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.

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

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 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.

Build and maintain scalable machine learning models, services, infrastructure, and tooling for real-time logistics optimization. Develop model-serving, training, monitoring, experimentation, and feature-engineering capabilities supporting vehicle-routing decisions at global scale. Collaborate with data scientists and engineers, evaluate technologies, investigate existing systems, leverage cloud infrastructure, and design highly available architectures for multiple teams.

As an ML Engineer, you'll build and deploy scalable ML systems, optimize models, and collaborate with teams to improve content across platforms.

As an ML Engineer Intern, you will build and deploy ML models, design scalable infrastructure, and collaborate with data science teams in a fast-paced environment.

The Senior ML Infrastructure Engineer will own and optimize GPU infrastructure for training tabular foundation models, ensuring high efficiency and collaboration with research teams.

As an ML Engineer, you'll deploy models and create tailored AI solutions for clients, ensuring impactful integrations and customer success.

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.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.