Machine Learning Data Engineer, Replica Pipelines

Team: Engineering

Location: Karlsruhe

Commitment: Full-time

Workplace Type: hybrid

About the role:

• We are hiring a Machine Learning Data Engineer responsible for building and scaling the data pipelines that support Replica and ML model development. You will ensure that data flows efficiently from raw customer inputs through validated, structured formats suitable for training, evaluation, and production systems.

What you'll do:

• Own data ingestion: Build reliable pipelines to normalize and validate customer and synthetic data.
• Define data standards: Create schemas, validation checks, and quality metrics for Replica datasets.
• Build curation tooling: Implement tools for dataset filtering, versioning, and annotation support.
• Enable ML workflows: Generate high-quality data feeds for training and evaluation across ML models.

What you’ll bring:

• Data engineering experience: Proven experience building scalable data pipelines and tooling.
• ML-aware engineering: Understanding of how data is used in model training and evaluation. 
• 3D Foundations: Practical experience with 3D concepts, geometry, and the linear algebra principles underpinning computer vision (e.g., projections, transformations)
• Technical skills: Strong Python proficiency and comfort with large datasets.
• Collaborative mindset: Experience working closely with ML engineers on data needs.

What will help you stand out:

• Advanced degree: MS or PhD in ML, computer vision, robotics, or related field.
• Cloud/infra experience: Familiarity with cloud storage and distributed processing frameworks.
• Robotics data knowledge: Experience handling camera, lidar, or radar data 
• Visualization tools experience: Familiarity with data visualization systems like Foxglove, Rerun, or Voxel51
• MLOps tooling exposure: Experience with dataset versioning, preprocessing automation, or training pipeline orchestration.

What we offer:

• Competitive compensation: Salary dependent on your skills, qualifications, experience, and location.
• Impactful work: The chance to contribute to the advancement of autonomous systems and AI.
• Collaborative culture: A dynamic and supportive work environment where your ideas are valued.
• Professional growth: Opportunities to learn and develop your skills in a cutting-edge field.