Senior Data Platform Engineer (Robotics)

Location: Irvine, California, United States

Company

hirify.global develops risk-aware, reliable, field-ready embodied AI systems for real robots, sensors, and deployments.

What you will do

• Design and build scalable data architecture for large-scale multimodal robotics and machine learning datasets.
• Develop ingestion, processing, metadata, lineage, dataset, and data-quality services.
• Build reliable pipelines that transform raw robot data into versioned, machine-learning-ready data products.
• Define schemas, data contracts, lifecycle states, provenance tracking, and lineage across processing workflows.
• Develop validation, observability, failure-diagnosis, reprocessing, backfill, and versioned-transformation capabilities.
• Collaborate with ML, robotics, labeling, infrastructure, and platform engineers on storage, compute, orchestration, reliability, and scalability.

Requirements

• Strong experience building production data platforms or large-scale data-processing systems.
• Strong software engineering skills in Python and/or C++, Java, or Go.
• Experience with distributed data processing and workflow orchestration.
• Experience with data lakes or lakehouses, object storage, metadata systems, schemas, and data versioning.
• Strong understanding of data quality, lineage, reproducibility, and reliable pipeline design.
• Experience with systems such as S3, Airflow or Dagster, Spark or Ray, Kubernetes, and Parquet, or similar technologies.

Nice to have

• Experience with machine learning datasets or ML infrastructure.
• Background in robotics, autonomous vehicles, sensors, video, images, LiDAR, or other multimodal data.
• Experience building internal developer or platform products.
• Experience operating pipelines at TB/PB scale.

Culture & Benefits

• On-site work in Irvine within Southern California’s robotics ecosystem.
• Work on real robots, sensors, and field deployments.
• Close collaboration across machine learning, research, labeling, robotics, infrastructure, and platform teams.
• Systems are tested on hardware and improved through real-world deployments.