Ofertas › Quantum-Systems GmbH
Quantum-Systems GmbH · Gilching, Germany · Remoto · 44.000 - 81.000 € per year
Salary: 44.000 - 81.000 € per year Requirements: Degree in Computer Science, Artificial Intelligence, Data Engineering, or a related technical discipline. Strong software engineering skills in Python, with experience building reliable, maintainable software and automated workflows for data-intensive machine learning systems. Several years of hands-on experience building production ML platforms, MLOps systems, or large-scale data and training pipelines. Solid knowledge of machine learning fundamentals and hands-on experience training and evaluating ML models. Deep understanding of the ML lifecycle, including dataset preparation, distributed training, experiment management, model validation, and reproducibility. Experience with cloud platforms and containerized infrastructure using Docker and Kubernetes. Experience with workflow orchestration, experiment tracking, and model or artifact management technologies such as Argo Workflows, Kubeflow, Metaflow, Airflow, MLflow, or comparable solutions. Practical experience with large datasets, object and shared storage, data formats, partitioning or sharding strategies, and high-throughput data loading. Familiarity with CI/CD, infrastructure as code, monitoring, logging, access control, and operating reliable production systems. Confident English communication skills and the ability to collaborate effectively across research, product, and infrastructure teams. High self-motivation and technical ownership from architecture through operation. Responsibilities: Design, implement, and operate scalable ML and data pipelines covering ingestion, dataset creation, training, and model validation. Build a reliable ML platform for reproducible experimentation and training across local, on-premises, and cloud GPU infrastructure. Develop efficient systems for large-scale multimodal datasets, including imagery, LiDAR, odometry, ground truth, and simulation data. Establish dataset and model versioning, metadata management, quality checks, and traceability from raw sensor data to validated model artifacts. Build and optimize distributed training workflows, job scheduling, GPU resource allocation, caching, and high-throughput data access. Develop AI-powered tools for internal data engineering, including semantic data search, metadata enrichment, automatic annotation, and quality assessment using foundation or multimodal models. Improve platform reliability, observability, security, cost efficiency, and developer experience. Collabor