Top Machine Learning Jobs in Berlin

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.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

Build foundational MLOps infrastructure and deploy/scale heavy-width CV and VLM models for luxury product authentication and fraud detection. Design data/feature management, model registry, serving, monitoring, and automated retraining pipelines. Evaluate tooling vs enterprise platforms, ensure low-latency high-throughput inference, and centralize ML engineering support across search, recommendations, pricing, and marketing while mentoring teams and enforcing engineering hygiene.

Lead and grow a ~50-person ML organization, prioritize high-impact AI projects, set ML technical direction and standards, ensure delivery and measurable business value, and collaborate across products to translate AI research into product innovations.

The AI/ML Engineer will develop and optimize machine learning models, work with cross-functional teams, and enhance mobile app features.

As an AI Research Engineer at Helsing, you'll architect and enhance deep learning frameworks, optimize infrastructure for distributed training, and develop strategies for large datasets while collaborating with a cross-functional team.

As an AI Research Engineer, you will develop signal processing solutions, collaborate across teams, and apply AI in real-world scenarios within defence.

Drive NVIDIA revenue growth within pharma, biotech and life sciences in DACH by building healthcare-specific solutions, cultivating strategic accounts and partner channels, leading sales execution, and coordinating cross-functional teams to expand pipeline and close enterprise deals.

Lead pre-sales technical discovery and serve as the primary architectural authority for enterprise AWS AI/ML, data, and cloud modernization initiatives. Design Bedrock and SageMaker solutions, migration strategies, and modern data platforms; develop AWS cost models, SOWs, proposals, and funding inputs; evaluate risks and compliance requirements; and coordinate technical handoffs with delivery teams. The role partners with account executives, AWS field teams, customers, and internal engineers without carrying a sales quota.

Build and operate production machine-learning systems for financial-crime transaction monitoring. Responsibilities include developing batch training pipelines, engineering AML and fraud features, deploying and monitoring models, evaluating drift and performance, calibrating thresholds, conducting sensitivity tests, and producing governance documentation. The role partners with AML investigators, fraud operations, product, and engineering teams to deliver explainable, auditable, and scalable ML solutions.

The Senior Technical Product Manager will shape product strategy for Constructor's platform, ensuring capabilities support scalability and security for learning applications.

Design, build and operate scalable ML infrastructure and pipelines on AWS to deploy, monitor and retrain models for personalization, recommendations, search and ranking. Collaborate with data scientists, engineers and product teams to ensure availability, observability and continuous improvement of production ML systems.

Develop, test, and deploy software for mobile manipulators focusing on perception, motion planning, control, sensor fusion, localization, and mapping. Integrate hardware (arms, grippers, sensors), optimize real-time system performance, troubleshoot hardware/software issues, and apply research and machine learning methods to improve task execution in production environments.

As a Senior Machine Learning Engineer, you will develop and scale customer-facing AI models, ensuring quality integration with business solutions, and mentoring peers.

Lead presales technical strategy for enterprise accounts: architect AI-infused planning solutions, run discovery sessions, deliver tailored demos and proofs-of-concept, coordinate cross-functional teams, and develop multi-year technology roadmaps to drive financial transformation.

Build and own end-to-end frontend and backend ML platform features, collaborate with product and research to productionize model and agent capabilities, and design scalable, reliable, and secure data science tools.

As a Senior AI Engineer, you will develop AI agents, integrate features into a web app, enhance matching systems, and collaborate across teams to deliver ML solutions.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

Design, build, and operate production-grade generative AI systems: multi-agent orchestration, end-to-end RAG pipelines, LLM evaluation and integration, scalable AWS-based AI infrastructure, CI/CD for models, observability, data validation, and evaluation frameworks to ensure quality, attribution, and resilience.

Join as the founding engineer to own architecture and ship features for an AI-driven adaptive learning platform. Build full-stack (Next.js/TypeScript/Python), implement LLM agents and RAG pipelines, iterate with schools, and shape technical strategy and hiring as an equity-holding co-founder.

Lead and mentor a team of Applied Scientists and AI/ML Engineers to design and deliver production-grade AI solutions across structured CRM and unstructured data. Drive technical strategy for agentic workflows, RAG/vector DB systems, and real-time reasoning. Partner with AI Platform on LLMOps and cloud infrastructure, act as a technical authority, and contribute as a high-impact individual contributor to ship customer-focused AI products.

Develop intelligent agents for a materials discovery engine, orchestrating scientific workflows and integrating experimental design for sustainability solutions.