Top Machine Learning Jobs in Berlin

Own end-to-end delivery of specific ADAS features (e.g., TFL, High Beam Assist, Blind Spot Monitoring). Define feature scope, set performance targets, align OEM requirements with internal roadmaps, collaborate with engineering/product/OEM teams, participate in testing/validation, and mitigate risks to ensure features meet specs and production readiness.

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.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Design and build scalable, low-latency backend APIs and product-stream ranking systems using typed languages. Integrate with data platforms and BI, manage large datasets, collaborate with engineers, PMs, and data scientists, and contribute to new features and technical improvements.

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 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 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.

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.

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.

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.

Develop, deploy, and maintain production AI/ML and Generative AI models on GCP for defense operations. Build MLOps infrastructure, RAG and multi-agent systems, LLM capabilities, secure model endpoints, CI/CD pipelines, evaluation frameworks, governance, and explainable AI controls. Optimize models and infrastructure for HPC, TPU, GPU, and edge environments while advising mission stakeholders and coaching teams on cloud ML integration.

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.

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.

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.

Own and scale Lounges personalization: build feature and training pipelines, production batch/real-time inference, A/B testing and attribution, integrate models into messaging (e.g., Braze), and mentor engineers while driving production-readiness and reliability.

Identify, qualify, and nurture enterprise leads for Cohere's AI solutions through outbound outreach, BANT qualification, Salesforce pipeline management, and collaboration with marketing. Run smaller sales cycles independently and stay current on product and industry trends to position solutions effectively.