Job description

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. 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 and implement or tune custom kernels where needed - Build benchmarking and regression detection infrastructure: latency baselines, throughput curves, and automated detection of performance regressions across model versions - Collaborate closely with research engineers to translate model innovations into optimized, deployment-ready implementations What We're Looking For - 3+ years of experience in inference optimization, ML systems, or a closely related field - Deep 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 inference - Experience with model optimization techniques: quantization (INT8/FP8/AWQ), distillation, pruning, and compilation - Familiarity 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 improvements - High ownership mindset and comfort in a fast-moving environment Nice 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 constraints - Experience with speculative decoding, continuous batching, or other LLM serving optimizations - Background in streaming or low-latency systems relevant to real-time robot control Why This Role - Direct 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 behavior - Own the optimization layer that determines how quickly and efficiently our foundation models run in the real world — high ownership, high impact, small elite team

Requirements

Must-haves

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 and implement or tune custom kernels where needed
Build benchmarking and regression detection infrastructure: latency baselines, throughput curves, and automated detection of performance regressions across model versions
Collaborate closely with research engineers to translate model innovations into optimized, deployment-ready implementations
3+ years of experience in inference optimization, ML systems, or a closely related field
Strong understanding of compute, memory bandwidth, and I/O bottlenecks in large model inference
Experience with model optimization techniques: quantization (INT8/FP8/AWQ), distillation, pruning, and compilation
Familiarity 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 improvements
High ownership mindset and comfort in a fast-moving environment
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 constraints
Experience with speculative decoding, continuous batching, or other LLM serving optimizations
Background in streaming or low-latency systems relevant to real-time robot control
Why This Role
Direct 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 behavior
Own the optimization layer that determines how quickly and efficiently our foundation models run in the real world — high ownership, high impact, small elite team

Nice-to-haves

Deep hands-on experience with modern ML stacks (PyTorch required; JAX a plus)
Nice to Have (But Not Required)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlllm

Platform: ashby
Effort: quick

Benefits

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Vision