rhoda-ai · Mountain View
Skills: AWS
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. Running large multimodal foundation models for a growing fleet of robots is expensive. We can't rely on one hardware vendor or the most expensive accelerators. We're looking for an Inference Optimization engineer to make Rhoda's models run fast, correctly, and cheaply on a wide range of hardware: AMD Instinct GPUs, Google TPUs, wafer-scale and dataflow accelerators, consumer GPUs like the RTX 5090, and the Jetson/Thor-class compute on our robots. You'll decide which chip runs which workload, and then make sure it runs well there. You'll work closely with the research, robotics, and infrastructure teams. WHAT YOU'LL DO - Own cost-efficient inference end-to-end. Make Rhoda's foundation models (vision, video, language, proprioception, action) faster and cheaper to run, measured in latency, throughput, and cost per inference. - Port and optimize models for non-NVIDIA and lower-cost hardware. This includes AMD (ROCm, HIP, MIOpen/hipBLASLt, Composable Kernel), TPU (XLA, JAX, Pallas, PyTorch/XLA), wafer-scale and dataflow accelerators (their SDKs and compilers), and consumer Blackwell GPUs such as the RTX 5090 (FP8/FP4, small memory, multi-GPU setups over PCIe). - Own on-robot inference. Deploy and optimize models on Jetson/Thor-class embedded compute under tight latency, power, thermal, and memory limits, and decide how work is split between the…
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