Software Engineer, Data Platform

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

About Mind:

Mind Robotics is building robots that learn from real-world experience, and the data platform is what turns raw experience into training signal. Every day, sensor data flows in from multiple sources — arriving in different formats, at different volumes, and with different quality bars. This team owns what happens next: ingesting that data reliably, validating and curating it for quality and diversity, powering annotation, and building the automated methods that will increasingly do that labeling work.

About the role:

As a Software Engineer working on the data platform, you'll be one of the founding engineers on the team that owns our application layer, reporting to our Head of Application Engineering. The systems work today; your job is to help harden and productionize them for scale — ingesting from a growing number of sources and field sites, and turning raw captures into training-ready data faster and at higher quality. This is early, hands-on, 0-to-1 engineering: you'll build the pipelines and tooling that determine what data actually makes it into our models, and see the effect on robot behavior.

You will:

• Build data ingestion pipelines from multiple sources, including our own field capture and teleop stacks

• Build automatic data validation systems that catch quality issues before data reaches annotation or training

• Build and improve annotation ingestion, tooling, and workflows to increase labelling efficiency and throughput

• Own data quality and diversity — build the systems and metrics that tell us what data we have, what's missing, and where to focus collection

• Explore and build automated annotation methods, including running computer vision and vision-language models for tasks like depth estimation, hand tracking, and auto-labeling, to reduce manual labelling load over time

• Support the platform — debug issues in live pipelines, instrument for observability, and work directly with research and annotation partners who depend on your software

Requirements:

• 2+ years of software engineering experience building production systems

• Strong programming fundamentals and comfort working across the stack — services, data pipelines, and applied ML tooling

• Experience with at least one of: real-time or large-scale data pipelines, ML data workflows, computer vision, or data infrastructure

• Bias for ownership: you've taken features or systems from prototype to production and supported them in the field

• Clear communication and close collaboration with product, research, and annotation/operations partners

• Hands‑on experience with sensor data (video, depth, IMU, force/torque) and the infrastructure to process, validate, and label it at scale is a plus

• Experience with streaming or near‑real‑time data pipelines is a plus

• Familiarity with ML data workflows (datasets, labelling, evaluation) is a plus

• Experience running or integrating computer vision or vision‑language models (e.g., depth, pose/hand tracking, open‑vocabulary detection, auto‑labeling) is a plus