Research Engineer - Data Infrastructure

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Staff Plus From the posting source checked Jun 20, 2026

Schedule

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Weekend work

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Company

Company stage

Growth-stage From the posting source checked Jun 20, 2026

Application

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Assessment

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Deadline

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State eligibility is not yet verified.

About this role

Research Engineer - Data Infrastructure Palo Alto, California, United States 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 $400M 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 -

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