Research Member of Technical Staff - Efficient Modeling

About the Company

Rhoda AI is building the next generation of generalist intelligent robots. We own the full robotics stack, from high-performance hardware to state-of-the-art foundation world models. Having raised over $450M, we are investing aggressively in model research, infrastructure, and manufacturing to make generalist robotics a reality.

Responsibilities

• Research and implement model compression techniques including quantization, pruning, structured sparsity, distillation, and low-rank approximation
• Design efficient architectures and attention mechanisms for real-time inference on edge and robot hardware
• Develop training strategies to optimize accuracy-efficiency tradeoffs
• Profile and benchmark models across hardware targets to resolve efficiency bottlenecks
• Build evaluation frameworks to measure capability retention after compression
• Collaborate with training and deployment teams to ensure efficient real-world inference
• Publish and present work at top-tier research venues

Requirements

• Strong understanding of model compression and efficient architectures for large models
• Hands-on experience with quantization, distillation, or pruning applied to transformers
• Deep knowledge of efficiency gains in modern architectures
• Proficiency with PyTorch
• Familiarity with hardware-aware optimization such as CUDA or TensorRT
• Ability to run principled experiments characterizing capability-efficiency tradeoffs

Preferred Qualifications

• PhD in ML, CS, or a related field
• Publication record at NeurIPS, ICML, ICLR, or MLSys
• Experience with efficient video or multimodal model architectures
• Familiarity with edge deployment targets like 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