Rhoda AI · San Francisco
OtherResearchFullTime
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
Experience optimizing data throughput, workload balancing, and cost-performance tradeoffs in cloud environments
Experience with distributed compute frameworks such as Ray or Spark for large-scale data processing and transformation
Strong skills in observability, monitoring, and production reliability for high-scale systems
Strong software engineering fundamentals with the ability to own systems end-to-end, from design to production
Staff-level candidates are expected to define technical direction and own architectural decisions independently; senior candidates execute complex systems work with strong fundamentals and growing scope
Nice to Have (But Not Required)
Experience managing large multimodal datasets
Familiarity with ML training workflows and data lifecycle management
Familiarity with vision-language models (VLMs) and experience running ML inference workloads at scale in distributed or cloud environments
Experience with robotics data formats or real-world sensor data (video, proprioception, teleoperation logs)
Experience with data warehouse technologies (e.g., Snowflake, BigQuery, or Redshift) for large-scale data storage, querying, and analytics
Familiarity with data versioning and lineage tooling (e.g., DVC, Delta Lake, or similar)
Why This Role
Own the data foundation that everything else runs on — model quality is only as good as the data infrastructure beneath it
Direct collaboration with research and ML systems teams; your work has immediate, measurable impact on training velocity
High ownership in a small team — you'll make real architectural decisions, not execute tickets
Help build the infrastructure that powers robots operating in the real world, at scale