Research Member of Technical Staff - Data Infrastructure

About the Company

Rhoda AI is building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up.

Responsibilities

• Architect, build, and scale a high-throughput data infrastructure for billions of video clips
• Design and optimize large-scale storage systems for multimodal datasets
• Build efficient indexing and retrieval systems for fast dataset querying and filtering
• Develop observability frameworks including monitoring, alerting, and failure recovery
• Implement intelligent workload balancing and throughput optimization across distributed systems
• Manage data artifacts, versioning, and lineage to ensure reproducibility
• Build internal tools for researchers to explore and analyze large datasets
• Support integration of vision-language models (VLMs) within data pipelines

Requirements

• 5+ years of experience in data infrastructure, distributed systems, or ML infrastructure
• Experience building and operating large-scale data pipelines (1B+ samples or petabyte-scale)
• Deep understanding of distributed systems, databases, and cloud storage architectures
• Experience optimizing data throughput and cost-performance tradeoffs in cloud environments
• Experience with distributed compute frameworks such as Ray or Spark
• Strong software engineering fundamentals and production reliability skills

Preferred Qualifications

• Experience managing large multimodal datasets
• Familiarity with ML training workflows and data lifecycle management
• Experience running ML inference workloads at scale
• Experience with robotics data formats or real-world sensor data
• Experience with data warehouse technologies like Snowflake, BigQuery, or Redshift
• Familiarity with data versioning and lineage tooling such as DVC or Delta Lake