Senior Data Platform Engineer

We are building the data foundation that powers the full machine learning lifecycle for autonomous robotics.

Our robots generate large-scale, multimodal datasets across real-world deployments, and turning that raw experience into reliable, discoverable, high-quality ML data is a core part of improving our autonomy systems.

As a Data Platform Engineer, you will help design and build the platform that manages data from ingestion through processing, validation, labeling, dataset generation, training, and evaluation.

This is not a traditional analytics data engineering role.

You will work closely with ML engineers, researchers, labeling teams, robotics engineers, and infrastructure engineers to build scalable systems for robotics and ML data.

• Design and build scalable data architecture for large-scale multimodal robotics and ML datasets.
• Build abstractions and services for ingestion, processing, datasets, metadata, lineage, and data quality.
• Develop reliable pipelines for transforming raw robot data into versioned, ML-ready data products.
• Define data models, schemas, contracts, and lifecycle states across data-processing workflows.
• Build systems for tracking provenance and lineage across raw data, derived artifacts, labels, datasets, and downstream ML workloads.
• Develop automated validation and data-quality frameworks that detect incomplete, corrupted, or unusable data early.
• Design for incremental processing, reprocessing, backfills, and versioned transformations.
• Improve observability and failure diagnosis across complex data workflows.
• Partner with ML and robotics teams to understand domain-specific data requirements and turn recurring patterns into reusable platform capabilities.
• Work closely with infrastructure/platform teams on storage, compute, orchestration, reliability, and scalability.
• Strong experience building production data platforms or large-scale data-processing systems.
• Strong software engineering skills, preferably Python and/or C++/Java/Go.
• Experience with distributed data processing and workflow orchestration.
• Experience with data lakes/lakehouses, object storage, metadata systems, schemas, and data versioning.
• Strong understanding of data quality, lineage, reproducibility, and reliable pipeline design.