Redefining Robotic Intelligence. Backed by Khosla.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 2+ years of experience in inference optimization, ML systems, ML compilers, or a closely related field
- Deep hands-on experience with PyTorch
- Strong understanding of compute, memory bandwidth, memory capacity, and interconnect bottlenecks, and how they change from one hardware architecture to another
- Experience with model optimization techniques: quantization (INT8/FP8/FP4, AWQ), distillation, pruning, and compilation (torch.compile, XLA, TensorRT, IREE/MLIR, TVM, or vendor compilers)
- Familiarity with inference serving frameworks and their support for different hardware (e.g. vLLM on ROCm/TPU, SGLang, JetStream, Triton Inference Server)
- Excellent debugging and measurement skills. You can take "this is slow on chip X" or "this costs too much" and turn it into clear bottlenecks, experiments, and validated improvements
More about this role
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...
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