Robotics Data Infrastructure Engineer

Mind Robotics; Palo Alto (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Mind Robotics in Palo Alto is seeking a Data Infrastructure Engineer to design and operate the pipelines that convert raw robot sensor data into training-ready datasets. You will own individual data services, from ingestion to dataloaders, and help scale the architecture for real-world industrial use.

You will collaborate with researchers and engineers to ensure data quality, implement auto-labeling and validation workflows, and build distributed processing systems for batch and streaming

Qualifications

• 2+ years of software engineering experience with data pipelines.
• Strong Python production-grade data processing skills.
• Experience with at least one distributed processing framework.
• Familiarity with object storage and data lake formats.
• Ability to collaborate with researchers and scale pipelines.

Responsibilities

• Build and scale ingestion pipelines for high-volume sensor data.
• Maintain data quality and curation systems, including workflows and auto-labeling.
• Contribute to storage and retrieval design for large-scale multimodal datasets.
• Build and operate distributed processing infrastructure (batch and streaming).
• Debug production issues in live data pipelines, handling data drift and failures.
• Work with modeling/research partners to understand data needs and translate them into pipelines.
• Participate in design and code reviews across the data infrastructure stack.

Skills

Python

Data pipelines

Distributed processing

Backend systems

Ownership mindset

Communication

Tools

Airflow

Dagster

Prefect

Spark

Ray

Job description

Mind Robotics in Palo Alto is seeking a Data Infrastructure Engineer to design and operate the pipelines that convert raw robot sensor data into training-ready datasets. You will own individual data services, from ingestion to dataloaders, and help scale the architecture for real-world industrial use.

You will collaborate with researchers and engineers to ensure data quality, implement auto-labeling and validation workflows, and build distributed processing systems for batch and streaming