Research Member of Technical Staff- Efficient Modeling
Location
Mountain View
Work mode
on-site
Type
full-time
Department
Research
Last seen
23h ago
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
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Research and implement model compression techniques: quantization, pruning, structured sparsity, distillation, and low-rank approximation
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Design efficient architectures and attention mechanisms suited to real-time inference on edge and robot hardware
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Develop training strategies that produce better accuracy-efficiency tradeoffs from the start
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Profile and benchmark models across hardware targets to identify and resolve efficiency bottlenecks
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Build evaluation frameworks that measure capability retention after compression or architecture changes
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Collaborate with training systems and deployment teams to ensure efficient models translate to faster real-world inference
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Publish and present work at top-tier venues
What We’re Looking For
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Strong understanding of model compression and efficient architectures for large models
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Hands-on experience with quantization, distillation, or pruning applied to transformers or large neural networks
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Deep knowledge of where efficiency gains are possible in modern architectures
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Proficiency with PyTorch and familiarity with hardware-aware optimization (CUDA, TensorRT, or similar)
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Ability to run principled experiments that characterize capability-efficiency tradeoffs
Nice to Have (But Not Required)
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PhD in ML, CS, or a related field — or equivalent research/engineering experience
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Publication record at NeurIPS, ICML, ICLR, MLSys, or related venues
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Experience with efficient video or multimodal model architectures
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Familiarity with edge deployment targets (Jetson, custom ASICs, or mobile hardware)
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Prior work on speculative decoding, early exit, or adaptive compute
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Experience deploying compressed models on physical robots or latency-constrained systems
Why This Role
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Bridge the gap between large-scale research models and real-time robot deployments
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Your work determines whether frontier capabilities actually run on our hardware
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High leverage: efficiency improvements benefit every model the team trains and deploys
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Work at a rare intersection of deep learning research and systems