Senior) ML Platform Engineer; m​/f​/d

As a Senior Machine Learning Platform Engineer (m/f/d), you will design and build the platform that turns large-scale UxV and sensor data into validated, reproducible machine learning models.

You will own and improve the end-to-end ML lifecycle—from data processing, selection, and annotation through distributed model training, experiment tracking and validation. Your work will enable ML engineers to develop autonomy and perception capabilities efficiently, reliably, and at scale.

Beyond conventional platform engineering, you will also apply modern AI models to our internal data workflows. This may include semantic dataset search, automated metadata generation, data curation, and auto-labeling using foundation and multimodal models.

By creating the foundations on which our AI teams build and validate models, you will directly contribute to Quantum Systems’ next generation of autonomous UxV solutions.

• 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.

• Collaborate with ML, data, and engineering teams to translate their technical needs into scalable platform capabilities.

• Mentor engineers, contribute to architectural decisions, and establish engineering standards for production-grade ML systems.

• You hold a degree in Computer Science, Artificial Intelligence, Data Engineering, or a related technical discipline.

• You have strong software engineering skills in Python and experience building reliable, maintainable software and automated workflows for data-intensive machine learning systems.

• You have 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.

• You have a deep understanding of the ML lifecycle, including dataset preparation, distributed training, experiment management, model validation, and reproducibility.

• You have experience with cloud platforms and containerized infrastructure using Docker and Kubernetes.

• You have worked with workflow orchestration, experiment tracking, and model or artifact management technologies such as Argo Workflows, Kubeflow, Metaflow, Airflow, MLflow, or comparable solutions.

• You have practical experience with large datasets, object and shared storage, data formats, partitioning or sharding strategies, and high-throughput data loading.

• You are familiar with CI/CD, infrastructure as code, monitoring, logging, access control, and operating reliable production systems.

• You communicate confidently in English and collaborate effectively across research, product, and infrastructure teams.

• You are highly self-motivated and take technical ownership from architecture through operation.

• We believe in the power of combined efforts:…