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