Research Engineer- Data Infrastructure

At Rhoda AI, we're building the full-stack foundation for the next generation of humanoid robots — from high-performance, software-defined hardware to the foundational models and video world models that control it. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling scenarios unseen in training. We work at the intersection of large-scale learning, robotics, and systems, with a research team that includes researchers from Stanford, Berkeley, Harvard, and beyond. We're not building a feature; we're building a new computing platform for physical work — and with over $400M raised, we're investing aggressively in the R&D, hardware development, and manufacturing scale-up to make that 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