Robotics Data Infrastructure Engineer

Mind Robotics; Palo Alto (CA); On-site; USD 120,000 - 190,000; Full time

Job summary

Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We are seeking a Data Infrastructure Engineer to build and operate pipelines that transform raw sensor and demonstration data into training-ready datasets.

You will own specific pipelines from ingestion to dataloaders for model training, contributing to storage design, batch and streaming processing, and data quality workflows.

Qualifications

• 2+ years of software engineering with data pipelines, data engineering, or backend systems.
• Strong Python programming; production-grade data processing.
• Experience with at least one distributed data processing framework (Spark, Ray, Dask, or Flink) or readiness to learn.
• Familiarity with object storage, data lake formats, and warehouse vs. lake tradeoffs.
• Proven ownership: shipped features or systems to production.
• Good communication with research/modeling teams and senior teammates.

Responsibilities

• Build and scale ingestion pipelines for high-volume, high-dimensional sensor data.
• Maintain data quality and curation systems with manual reviews and auto-labeling.
• Contribute to storage and retrieval design for large multimodal datasets, including versioning/lineage.
• Build and operate distributed processing infrastructure (batch and streaming).
• Debug production issues in live data pipelines: data corruption and schema drift at scale.
• Collaborate with modeling partners to translate data needs into pipelines.
• Participate in design and code reviews across the data infra stack.

Skills

Python

Data pipelines

Distributed processing

Data quality

Ownership

Tools

Airflow

Dagster

Prefect

Job description

Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We are seeking a Data Infrastructure Engineer to build and operate pipelines that transform raw sensor and demonstration data into training-ready datasets.

You will own specific pipelines from ingestion to dataloaders for model training, contributing to storage design, batch and streaming processing, and data quality workflows.