At Rhoda AI, we’re 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. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality.

We're looking for Data Infrastructure MLEs to scale the systems that power our model training data pipeline, from raw ingestion and storage to indexing, retrieval, and throughput optimization at massive scale. We hire across levels — from senior to staff.

What You'll Do

• Architect, build, and scale a high-throughput data infrastructure that processes and manages billions of video clips with strong guarantees around reliability, latency, and cost efficiency
• Design and optimize large-scale storage systems (cloud object storage, databases, metadata stores) for multimodal datasets
• Build efficient indexing and retrieval systems to support fast dataset querying, filtering, and iteration for research and production use cases
• Develop observability frameworks for data pipelines including monitoring, alerting, failure recovery, and performance optimization
• Implement intelligent workload balancing and throughput optimization across distributed compute and storage systems
• Manage data artifacts, versioning, and lineage to ensure reproducibility and traceability across training runs
• Build internal interfaces and lightweight tools that enable researchers and engineers to explore, query, and analyze large datasets at scale
• Support integration and scalable deployment of vision-language models (VLMs) within data pipelines for screening, enrichment, or metadata generation

What We're Looking For

• 5+ years of experience in data infrastructure, distributed systems, ML infrastructure, or a closely related field
• Strong experience building and operating large-scale data pipelines (1B+ samples or petabyte-scale systems preferred)
• Deep understanding of distributed systems, databases, indexing strategies, and cloud storage architectures