Inference Optimization ML Engineer

Benefits

Parental leave

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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

Mid 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

Inference Optimization ML Engineer 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 an Inference Optimization MLE to help build and operate the systems that make our foundation models run fast and efficiently in production. You'll be responsible for squeezing maximum performance out of large multimodal models, across cloud and on-robot deployment targets. You will working closely with research and robotics teams to close the gap between training and real-world deployment. What You'll Do - Own inference performance end-to-end - diagnose and improve latency, throughput, and efficiency of large foundation models in production - Build systematic performance attribution: latency decomposition (compute vs. memory bandwidth vs. I/O), bottleneck identification, and prioritization across model families - Apply and develop optimization techniques including quantization, pruning, distillation, operator fusion, and model compilation (e.g., TensorRT, torch.compile, XLA) - Optimize attention mechanisms, KV caching, and memory layouts for large multimodal models (vision, video, language, proprioception) - Work with kernel-level tooling (e.g., CUDA, Triton) to identify hotspots

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