Research Member of Technical Staff- Data Infrastructure

Rhoda AI; Mountain View (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Rhoda AI is building generalist robotics systems and scalable data infrastructure to support billions of video samples. The role focuses on architecting high-throughput data pipelines, scalable storage, and robust data lineage for research and production.

You will own end-to-end data systems, collaborate with ML teams, and optimize cost and performance while enabling rapid iteration on vision‑language models and datasets at scale.

Qualifications

• 5+ years of experience in data infrastructure or ML infrastructure.
• Experience building large-scale data pipelines (1B+ samples or petabyte-scale).
• Deep understanding of distributed systems, databases, and cloud storage.
• Experience optimizing throughput and cost-performance in cloud environments.
• Experience with Ray or Spark for large-scale processing.
• Strong observability and production reliability skills.
• Solid software engineering fundamentals with end-to-end ownership.
• Staff level candidates should define direction independently; seniors execute complex work.

Responsibilities

• Architect, build, and scale a high-throughput data infrastructure processing billions of video clips with reliability, low latency, and cost efficiency.
• Design and optimize large-scale storage systems for multimodal datasets (cloud object storage, databases, metadata stores).
• 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, and failure recovery.
• Implement workload balancing and throughput optimization across distributed compute and storage systems.
• Manage data artifacts, versioning, and lineage for reproducibility across training runs.
• Build internal interfaces and lightweight tools for researchers to explore large datasets at scale.
• Support integration and scalable deployment of vision‑language models within data pipelines.

Skills

Distributed systems

Large-scale pipelines

Cloud storage

Ray

Spark

Observability

End-to-end ownership

System design

Tools

Snowflake

BigQuery

Delta Lake

DVC

Job description

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.

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