Top Machine Learning Jobs in Berlin

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 and grow a team of data analysts in AdTech, shifting them from reactive reporting to proactive, revenue-focused partners. Own delivery processes, prioritize high-ROI projects, manage Director-level stakeholders, translate Ads strategy into measurable goals, ensure technical QA of SQL and dashboards, and operationalize ML models with data science partners to maximize ad yield and vendor ROI.

Own the vision, roadmap, development, experimentation, and performance monitoring of data products powering dynamic assortment and personalized digital shelf experiences. Partner with data science, engineering, merchandising, analytics, and commercial teams to deploy machine learning models, run A/B tests, establish KPIs, and improve conversion, average order value, retention, and customer satisfaction.

Develops new business for AI-driven software solutions by identifying prospects, managing the full sales cycle, presenting proposals, negotiating contracts, maintaining CRM records, and transitioning new accounts to delivery teams. The role also builds long-term customer relationships, expands professional networks, analyzes AI market opportunities, and supports revenue growth through complex proposals and tenders.

Lead redesign and development of B2B underwriting models, scorecards and decision engines; improve data analysis and processes; build multi-country credit frameworks; partner with Data Science to deploy ML credit models; enhance reporting and risk KPIs; collaborate with cross-functional teams to support B2B product expansion.

Analyze and manage customer credit risk for BNPL products across Europe, optimize consumer portfolios via business rules, improve data analysis and reporting, support risk steering platform development, and advise Key Account Management on risk measures.

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

The role involves sourcing and evaluating AI infrastructure founders, supporting them through early stages of investment, and representing the firm with technical expertise.

Lead Workday Compensation implementations and HCM transformations: run workshops, gather requirements, configure, test, deploy, lead workstreams, liaise with clients and teams, and apply Accenture/Workday methodologies and AI-enabled tools.

Develop and maintain a TypeScript/JavaScript fullstack IoT platform dashboard (React, Koa/Express), collaborate on microservices, write high-quality code and tests, and contribute to product and architecture decisions in an agile team.

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.

As an ML Research Engineer, you will develop computational methods and infrastructure to enhance molecular simulations and machine learning force fields for materials discovery.

Lead and grow multiple small, high-output engineering teams focused on AI-powered vulnerability management. Stay hands-on with architecture and code reviews, drive hiring and career development, ensure technical excellence, and coordinate cross-team priorities to deliver customer and revenue impact.

Lead the estimating product roadmap for an AI-powered construction SaaS: prioritize and ship MVPs, run rapid experiments, conduct user discovery with estimators, define success metrics, and iterate using data and user feedback.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

Lead CRM lifecycle strategy and execution to maximize retention and monetization. Design algorithmic personalization, run rigorous incrementality experiments, partner with Data/Engineering on real-time event schema, and build/operate omnichannel campaigns (Email, Push, SMS, In-App) including API-triggered flows.

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.

As a C++ Engineer, you will develop machine learning features, integrate models, maintain SDKs, and collaborate across teams for software performance and enhancements.

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.

Lead a cross-functional team of Data Scientists, MLOps Engineers, and Product to deliver horizontal data and ML products (including LLM-based systems). Own roadmap, hiring, performance, operationalization, adoption, and production reliability. Drive stakeholder partnerships, raise production quality standards, and balance exploration with scalable, business-impacting solutions.

Lead Rasa’s product organization and define strategy for its enterprise conversational AI platform. Manage and develop a team of product managers, own product roadmaps and lifecycle decisions, and partner with engineering, sales, and marketing on product definition, pricing, packaging, and go-to-market. Use customer research, market analysis, competitive intelligence, and developer feedback to guide innovation and foster a strong ecosystem across cloud, on-premises, and open-source deployments.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

Assess and evaluate AI-generated technical content, craft and answer advanced ML/AI questions, identify errors and hallucinations, rank responses by correctness and clarity, and provide precise feedback to improve model performance.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.