Research Member of Technical Staff- Efficient Modeling

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