Research Member of Technical Staff- Data Infrastructure

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