Top Machine Learning Jobs in Germany

Lead design and delivery of a greenfield Anti Financial Crime platform. Translate AML/AFC requirements into technical specs, own roadmap and architecture, collaborate with AML/AFC operations and cross-functional product and engineering teams, and drive scalable detection, prevention, and automation using data and ML-enabled solutions.

The PhD researcher will focus on AI-driven semantic structure extraction, automated reasoning modeling, and personalized content generation, supporting interdisciplinary research and teaching activities in AI and NLP.

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

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 the core biometric recognition technology team, managing the full lifecycle of ML systems focused on iris and face recognition while ensuring privacy and reliability at scale.

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.

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.

The role involves developing mobile applications for energy visualization, focusing on native Android and cross-platform solutions, with AI features integration.

Proxima Fusion seeks passionate candidates for unsolicited applications to contribute to innovative clean energy solutions and support the development of fusion technology.

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.

Design, build, and operationalize ML and data pipelines; retrieve, parse, and process structured and unstructured data; integrate large language models into analytic workflows; develop algorithms and production-ready AI/ML systems; collaborate with cross-functional teams and clients to deliver data-driven solutions for public and private sector missions. Secret clearance required.

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.

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.

As a Data & Analytics Analyst, you'll collaborate with clients to create tailored solutions, implement prototypes, and share ideas to solve analytical challenges.

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.

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.

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 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.

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.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.