Research Member of Technical Staff- Efficient Modeling

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 a Research Scientist or Research Engineer focused on model efficiency — making our foundation world models faster, smaller, and more deployable without sacrificing capability. This work is critical to closing the gap between research-scale models and real-time operation on robot hardware. What You'll Do • Research and implement model compression techniques: quantization, pruning, structured sparsity, distillation, and low-rank approximation • Design efficient architectures and attention mechanisms suited to real-time inference on edge and robot hardware • Develop training strategies that produce better accuracy-efficiency tradeoffs from the start • Profile and benchmark models across hardware targets to identify and resolve efficiency bottlenecks • Build evaluation frameworks that measure capability retention after compression or architecture changes • Collaborate with training systems and deployment teams to ensure efficient models translate to faster real-world inference • Publish and present work at top-tier venues What We're Looking For • Strong understanding of model compression and efficient architectures for large models • Hands-on experience with quantization, distillation, or pruning applied to transformers or large neural networks • Deep knowledge of where efficiency gains are possible in modern architectures • Proficiency with PyTorch and familiarity with hardware-aware optimization (CUDA, TensorRT, or similar) • Ability to run principled experiments that characterize capability-efficiency tradeoffs Nice to Have (But Not Required) • PhD in ML, CS, or a related field — or equivalent research/engineering experience • Publication record at NeurIPS, ICML, ICLR, MLSys, or related venues • Experience with efficient video or multimodal model architectures • Familiarity with edge deployment targets (Jetson, custom ASICs, or mobile hardware) • Prior work on speculative decoding, early exit, or adaptive compute • Experience deploying compressed models on physical robots or latency-constrained systems Why This Role • Bridge the gap between large-scale research models and real-time robot deployments • Your work determines whether frontier capabilities actually run on our hardware • High leverage: efficiency improvements benefit every model the team trains and deploys • Work at a rare intersection of deep learning research and systems