Senior Machine Learning Platform Engineer

Quick Facts

Role: Senior Machine Learning Platform Engineer

Focus: Scalable ML platform for validated, reproducible models from UxV and sensor data

Description

You will build and operate a platform that turns large-scale UxV and sensor data into validated, reproducible machine learning models used by AI teams for autonomy and perception. The work covers the full ML and data pipeline—from ingestion and dataset creation through distributed training, model validation, and platform operations across local, on-premises, and cloud GPU infrastructure.

Responsibilities

• Design, implement, and operate scalable ML and data pipelines for ingestion, dataset creation, training, and model validation.
• Build a reliable ML platform to enable reproducible experimentation and training across heterogeneous GPU environments.
• Develop efficient systems for large-scale multimodal datasets (imagery, LiDAR, odometry, ground truth, and simulation data).
• Set up dataset/model versioning, metadata management, quality checks, and traceability from raw sensor data to validated model artifacts.
• Optimize distributed training workflows, job scheduling, GPU resource allocation, caching, and high-throughput data access.
• Develop AI-powered internal data engineering tools (semantic data search, metadata enrichment, automatic annotation, quality assessment).
• Improve platform reliability, observability, security, cost efficiency, and developer experience.
• Collaborate with ML, data, and engineering teams to translate needs into scalable platform capabilities.
• Mentor engineers and contribute to architectural decisions and production-grade engineering standards.

Requirements

• Degree in Computer Science, Artificial Intelligence, Data Engineering, or a related technical discipline.
• Strong Python software engineering skills for reliable, maintainable systems and automated data-intensive ML workflows.
• Several years of hands-on experience building production ML platforms, MLOps systems, or large-scale data and training pipelines.
• Solid ML fundamentals, including training and evaluating ML models.
• Deep ML lifecycle knowledge: 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 and experiment/model artifact management (e.g., Argo Workflows, Kubeflow, Metaflow, Airflow, MLflow).
• Practical experience with large datasets, storage (object/shared), data formats, partitioning/sharding, and high-throughput data loading.
• Knowledge of CI/CD, infrastructure as code, monitoring, logging, access control, and operating production systems.
• Confident English communication and cross-team collaboration (research, product, infrastructure).
• High self-motivation and technical ownership from architecture through operations.

Benefits

• Company pension scheme
• Trust-based flexible working hours
• One flexible remote-work day per week
• Subsidized EGYM Wellpass membership
• Bike leasing
• Corporate discounts
• Employee events
• Sponsored daily lunch budget
• Shuttle from Laim in Munich
• Diversity-focused, inclusive workplace with growth opportunities