(Senior) ML Platform Engineer (m/f/d)

Job summary

Design and operate an end-to-end machine learning platform that transforms large-scale unmanned vehicle and sensor data into validated, reproducible models for autonomy and perception. Build scalable multimodal data pipelines, distributed training and experiment workflows across local, on-premises, and cloud GPU infrastructure, and AI-powered data tools such as semantic search and automated annotation using foundation or multimodal models.

Benefits

Qualifications

• Degree in Computer Science, Artificial Intelligence, Data Engineering, or a related technical discipline.
• Strong Python and software engineering skills, with experience building maintainable workflows for data-intensive machine learning systems.
• Several years of experience building production machine learning platforms, MLOps systems, or large-scale data and training pipelines.
• Knowledge of machine learning fundamentals and hands-on experience training and evaluating models.
• Understanding of 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 tools.
• Experience with large datasets, object and shared storage, data formats, partitioning or sharding, and high-throughput data loading.
• Familiarity with CI/CD, infrastructure as code, monitoring, logging, access control, and production operations.
• Confident English communication and effective cross-functional collaboration.
• Self-motivation and technical ownership from architecture through operation.

Responsibilities

• Design, implement, and operate scalable machine learning and data pipelines for ingestion, dataset creation, training, and model validation.
• Build reproducible experimentation and training infrastructure across local, on-premises, and cloud GPU environments.
• Develop systems for multimodal datasets, including imagery, LiDAR, odometry, ground truth, and simulation data.
• Establish dataset and model versioning, metadata management, quality checks, and traceability.
• Optimize distributed training, job scheduling, GPU allocation, caching, and high-throughput data access.
• Develop AI-powered tools for semantic data search, metadata enrichment, automated annotation, and quality assessment.
• Improve platform reliability, observability, security, cost efficiency, and developer experience.
• Collaborate with machine learning, data, and engineering teams to build scalable platform capabilities.
• Mentor engineers, contribute to architecture, and establish production engineering standards.

Skills

Degrees

Languages

Work schedule

Industry

Relocation