Data Infrastructure Engineer

About the team and the role:

Mind Robotics is building robots that learn from real-world experience. That starts with a data engine: the pipelines and infrastructure that turn raw, messy, multimodal sensor streams from robots and human demonstrations into high-quality, well-curated training data at scale.

As a Data Infrastructure Engineer, you'll build and operate the pipelines that turn raw sensor and demonstration data into training-ready datasets — from ingestion off real robots and capture devices, through processing and quality filtering, to the dataloaders that feed model training. The systems work today; your job is to build within the architecture, take ownership of specific pipelines and services, and help harden the system as it scales.

Responsibilities:

• Build and scale ingestion pipelines for high-volume, high-dimensional sensor data.
• Build and maintain data quality and curation systems, including both manual review workflows and auto-labeling.
• Contribute to storage and retrieval design for large-scale multimodal datasets, including format choices and versioning/lineage.
• Build and operate distributed processing infrastructure (batch and streaming).
• Debug production issues in live data pipelines — data corruption, schema drift, backpressure, and failures that only show up at scale.
• Work with modeling/research partners to understand data quality, format, and structure needs, and translate them into working pipelines.
• Participate in design and code review across the data infrastructure stack.

Requirements:

• 2+ years of software engineering experience, with some exposure to data pipelines, data engineering, or backend systems.
• Strong programming fundamentals in Python, with the ability to write performant, production-grade data processing code.
• Experience with at least one distributed data processing framework (e.g., Spark, Ray, Dask, or Flink), or strong fundamentals and willingness to ramp up quickly.
• Familiarity with data storage concepts — object storage, data lake table formats, and warehouse vs. lake tradeoffs.
• Bias for ownership: you've taken features or systems from prototype to production.
• Clear communicator who collaborates well with research/modeling partners and more senior teammates.
• Experience with workflow orchestration tools (e.g., Airflow, Dagster, Prefect) is a plus.
• Experience with streaming ingestion for high-volume, near-real-time data is a plus.
• Experience building automated data quality/curation systems (statistical filtering, anomaly detection, deduplication, or using ML models in an annotation/filtering pipeline) is a plus.
• Experience with robotics- or embodied-AI-specific data (multi-embodiment datasets, teleoperation/demonstration data, egocentric video) is a plus.