Research Scientist / Engineer - Training Systems

Benefits

Parental leave

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Non-birth-parent leave

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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)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

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

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Staff Plus From the posting source checked Jun 20, 2026

Schedule

Shift type

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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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Where they hire

State eligibility is not yet verified.

About this role

Research Scientist / Engineer - Training Systems Mountain View, Mountain View,, 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 a Staff / Principal ML Training Systems Engineer to own training systems performance end-to-end. You will define how our models train at scale - driving efficiency, scalability, and correctness across large-scale multimodal training. This is a core systems role, not infrastructure support. Your work directly determines how efficiently we use compute, how well models scale across thousands of GPUs, and how quickly research can iterate. What You'll Do Own training performance end-to-end - Diagnose and improve performance of large-scale multimodal training (vision, video, proprioception, actions, language) - Build systematic performance attribution: step-time decomposition (compute vs communication vs input pipeline), scaling curves across cluster sizes, and bottleneck identification and prioritization - Drive measurable gains in: - Distributed efficiency (comm/compute overlap, bucketization, topology-aware mapping, parallelism strategies) - Compute efficiency (kernel hotspots, operator fusion, attention optimization, framework/runtime overhead) - Memory efficiency (activation checkpointing, sequence packing/bucketing, fragmentation reduction)

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