Top Machine Learning Jobs in Berlin

Extract, curate, and ensure high-quality structured and unstructured oncology clinical data from EHRs. Develop and update curation instructions and capture forms, support QA processes, pilot new extraction approaches, contribute insights to product and ML/LLM-enabled curation, and collaborate with cross-functional teams to maintain and deliver high-quality datasets for research.

CrowdStrike seeks a Senior Intelligence Analyst to provide threat intelligence and digital risk monitoring, collaborating with teams to deliver customer-focused solutions and presentations, while maintaining customer relationships.

Lead organization-wide software and AI engineering strategy, design scalable cloud-native systems, drive deployment and data integration best practices, and deliver rapid prototype-to-production solutions. Own LLM and ML strategy including model fine-tuning, RAG, AI evaluation, and observability. Mentor technical leaders, shape developer experience and knowledge management, and represent engineering at executive and industry levels.

Sell Zscaler Zero Trust Branch solutions across DACH, owning regional quota, building account-based strategies, partnering with solution engineers, engaging C-suite and technical stakeholders to close complex deals for SD-WAN, data security, and advanced network products.

The Director of Business Development at Moloco will drive revenue growth, close partnerships, and support expansion across EMEA while focusing on AI-powered advertising solutions.

Design, implement, and test automation and robotic systems for machine vision and industrial applications. Develop embedded product designs and manufacturing processes, collaborate with R&D team, analyze test data, and maintain inspection and maintenance plans.

The Vice President, Product will define and scale the product vision for an AI-driven trade platform, overseeing product strategy and leading teams across multiple functions to enhance automation and address customer needs effectively.

As a Senior/Staff Product Manager, you will drive the strategy and execution for the marketplace, enhancing partner experiences and collaborating with cross-functional teams to optimize product offerings.

Design, build, and operate production AI systems for tax advisory: document intelligence, RAG over tax/legal knowledge, data pipelines, prediction and anomaly detection, and monitoring/evaluation. Collaborate with engineers and domain experts to deploy reliable, explainable AI that reduces manual tax work.

The Senior Data Product Manager drives product maturity and strategic growth at Seqana by translating user needs into technical requirements, overseeing product lifecycles, and exploring market opportunities.

As an Enterprise AI Adoption Manager, you will guide companies in adopting AI, manage enterprise accounts, drive AI transformation, and work closely with engineering and product teams to deliver value.

The Consulting Manager will lead AI transformation programs, work with executives on strategy, drive business growth, represent the company in discussions, and mentor teams.

Lead and manage two Voice teams (8 engineers) building ASR and voice AI products. Own technical direction, architecture decisions, roadmaps, hiring, performance management, and cross-team coherence for production ML systems.

The AI Business Development Specialist will identify business opportunities, build client relationships, and promote AI solutions in various sectors, with a focus on the MENA region.

Lead AI and machine learning architecture, mentor teams, drive innovation, and evaluate tools for AI systems development across global teams.

Lead the development of large language models for marketing technologies, optimize AI solutions, and mentor junior team members to drive innovation and quality.

Design, build, and productionize machine learning solutions on AWS. Work end-to-end from data exploration, feature engineering, modeling, and evaluation to packaging, deploying, monitoring, and iterating models (including LLM/GenAI workflows). Collaborate with engineers, product partners, and data scientists to deliver reliable, reproducible ML systems and move prototypes into scalable production.

Design and productionize ML systems for advertising using multimodal data (image, video, audio, text, structured). Improve MLOps and data platforms, build scalable data pipelines, monitor models, collaborate cross-functionally, and drive team processes and product impact.

Responsible for driving business development of AI products, managing the sales cycle, building client relationships, and ensuring contract negotiations are completed.

Lead FP&A for Germany: develop and maintain financial planning, controls, reporting, budgeting and forecasting. Manage FP&A team, financial analysis, valuation models, stakeholder partnering, and drive automation, digitization and multi-dimensional data models to enable reliable financial decision-making.

Develop core methods for temporal ontology extraction and contradiction detection. Collaborate with research teams and publish findings. Prototype ideas, create datasets, and validate methods.

Develop and validate analytical solutions for the Revenue Management system, build forecasting and pricing models, monitor system outputs and user interventions, drive stakeholder adoption, and use AI/ML tools to improve revenue optimization and commercial decision-making.

Lead the Affordability domain for Quick Commerce: define vision and roadmap, deliver item-level promotions and pricing solutions, align global stakeholders, partner with engineering and data science, run experiments, and measure impact on conversion, AOV, and retention.

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.

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.