Data Platform Engineer: Ingest, Validate & Auto-Label Pipelines

Mind Robotics Inc.; Palo Alto, Northern (CA, KY); Hybrid; USD 140,000 - 200,000; Full time

Job summary

Mind Robotics Inc. is seeking a Software Engineer to join the data platform team as a founding engineer. You’ll build ingestion pipelines, ensure data quality, and harden systems for scale, turning raw captures into training-ready data for our robotics models.

You’ll work across the stack, partner with research and annotation teams, and explore automated annotation methods, including CV and vision-language models, to reduce manual labeling over time.

Qualifications

• 2+ years of software engineering experience building production systems.
• Strong fundamentals across the stack: services, data pipelines, and ML tooling.
• Experience with real-time or large-scale data pipelines, ML data workflows, CV, or data infra.
• Bias for ownership: shipped features from prototype to production.
• Clear communication with product, research, and annotation partners.
• Hands-on sensor data experience (video, depth, IMU) is a plus.

Responsibilities

• Build data ingestion pipelines from multiple sources, including field capture.
• Build automatic data validation systems to catch quality issues before data reaches annotation or training.
• Improve annotation ingestion tooling to increase labeling throughput.
• Own data quality and diversity — build metrics to tell what data we have and what’s missing.
• Explore automated annotation methods, including CV and vision-language models to reduce manual labeling.
• Support the platform by debugging live pipelines and collaborating with research and annotation partners.

Skills

Production systems

Data pipelines

Cross-stack engineering

ML tooling

Real-time data

Tools

Python

Spark

GCP/AWS

Job description

Mind Robotics Inc. is seeking a Software Engineer to join the data platform team as a founding engineer. You’ll build ingestion pipelines, ensure data quality, and harden systems for scale, turning raw captures into training-ready data for our robotics models.

You’ll work across the stack, partner with research and annotation teams, and explore automated annotation methods, including CV and vision-language models, to reduce manual labeling over time.