Staff Data Infrastructure Engineer

Data Infrastructure MLEsAt 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 DoArchitect, build, and scale a high-throughput data infrastructure that processes and manages billions of video clips with strong guarantees around reliability, latency, and cost efficiencyDesign and optimize large-scale storage systems (cloud object storage, databases, metadata stores) for multimodal datasetsBuild efficient indexing and retrieval systems to support fast dataset querying, filtering, and iteration for research and production use casesDevelop observability frameworks for data pipelines including monitoring, alerting, failure recovery, and performance optimizationImplement intelligent workload balancing and throughput optimization across distributed compute and storage systemsManage data artifacts, versioning, and lineage to ensure reproducibility and traceability across training runsBuild internal interfaces and lightweight tools that enable researchers and engineers to explore, query, and analyze large datasets at scaleSupport integration and scalable deployment of vision-language models (VLMs) within data pipelines for screening, enrichment, or metadata generationWhat We're Looking For5+ years of experience in data infrastructure, distributed systems, ML infrastructure, or a closely related fieldStrong 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 architecturesExperience optimizing data throughput, workload balancing, and cost-performance tradeoffs in cloud environmentsExperience with distributed compute frameworks such as Ray or Spark for large-scale data processing and transformationStrong skills in observability, monitoring, and production reliability for high-scale systemsStrong software engineering fundamentals with the ability to own systems end-to-end, from design to productionStaff-level candidates are expected to define technical direction and own architectural decisions independently; senior candidates execute complex systems work with strong fundamentals and growing scopeNice to Have (But Not Required)Experience managing large multimodal datasetsFamiliarity with ML training workflows and data lifecycle managementFamiliarity with vision-language models (VLMs) and experience running ML inference workloads at scale in distributed or cloud environmentsExperience 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 analyticsFamiliarity with data versioning and lineage tooling (e.g., DVC, Delta Lake, or similar)Why This RoleOwn the data foundation that everything else runs on — model quality is only as good as the data infrastructure beneath itDirect collaboration with research and ML systems teams; your work has immediate, measurable impact on training velocityHigh ownership in a small team — you'll make real architectural decisions, not execute ticketsHelp build the infrastructure that powers robots operating in the real world, at scale