Top Machine Learning Jobs in Germany

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 finance transformation and pre-sales solution architecture for Workday Financials. Design end-to-end multi-country FIN solutions, lead workshops, drive executive alignment, enable sales, and shape post-go-live roadmaps using AI/automation to modernize finance operations.

As an AI Product Manager, you will define the product roadmap, collaborate with teams to launch AI features, and drive data-driven decisions.

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.

Manage various AI/ML projects from concept to development, coordinating teams, managing risks, and ensuring project goals are met. Responsible for documentation and financial planning.

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 design and build of a scalable internal data platform and central warehouse, own company-wide data architecture, mentor engineers, implement governance, observability, and quality controls, and enable AI adoption by creating pipelines and infrastructure for autonomous agent and ML use cases.

Lead DMPK-PD strategy and translational science across discovery and development. Drive advanced modelling (PKPD, PBPK, DDI), author regulatory nonclinical DMPK sections, implement biomarker/bioanalytical plans, and provide cross-functional leadership to de-risk programs and optimize clinical dosing.

Develop and apply AI/ML and advanced analytics to transform large structured and unstructured intelligence data into actionable insights. Perform data cleaning, integration, validation, and visualization; build automated analytical methodologies and ML models; produce dashboards in Tableau and Power BI; apply intelligence tradecraft (ABI); collaborate with analysts, engineers, and government stakeholders; document methodologies and support continuous process improvement.

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.

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.

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.

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.

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.

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.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Create and review realistic, locale-specific robotics and natural sciences tasks in the target language, apply scoring rubrics, evaluate AI and human responses for accuracy and regulatory alignment, and produce high-quality gold-standard solutions and feedback.