Senior Inference Optimization ML Engineer

Senior Inference Optimization ML Engineer LocationMountain ViewEmployment TypeFull timeDepartmentSoftwareCompensation$175K – $250K • Offers EquityAt 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.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 DoOwn inference performance end-to-end — diagnose and improve latency, throughput, and efficiency of large foundation models in productionBuild systematic performance attribution: latency decomposition (compute vs. memory bandwidth vs. I/O), bottleneck identification, and prioritization across model familiesApply 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 and implement or tune custom kernels where neededBuild benchmarking and regression detection infrastructure: latency baselines, throughput curves, and automated detection of performance regressions across model versionsCollaborate closely with research engineers to translate model innovations into optimized, deployment-ready implementationsWhat We're Looking For3+ years of experience in inference optimization, ML systems, or a closely related fieldDeep hands-on experience with modern ML stacks (PyTorch required; JAX a plus)Strong understanding of compute, memory bandwidth, and I/O bottlenecks in large model inferenceExperience with model optimization techniques: quantization (INT8/FP8/AWQ), distillation, pruning, and compilationFamiliarity with inference serving frameworks (e.g., Triton, TensorRT, vLLM, TorchServe)Exceptional debugging and measurement ability: turn "inference is slow" into clear bottlenecks, experiments, and validated improvementsHigh ownership mindset and comfort in a fast-moving environmentNice to Have (But Not Required)GPU kernel or compiler-level experience (CUDA, Triton, graph capture, operator fusion)Experience with multimodal or video model inference (variable-length sequences, packing/bucketing)Familiarity with edge/cloud hybrid deployment patterns and on-robot inference constraintsExperience with speculative decoding, continuous batching, or other LLM serving optimizationsBackground in streaming or low-latency systems relevant to real-time robot controlWhy This RoleDirect leverage on research velocity and real-world robot performance — every efficiency gain you make accelerates model iteration and tightens the loop between model and robot behaviorOwn the optimization layer that determines how quickly and efficiently our foundation models run in the real world — high ownership, high impact, small elite teamApply for this Job