Distillation Lead

You will…

• Define and drive the technical strategy for model distillation and compression across Waabi's AI stack — spanning perception, world models, and planning — with an eye toward both onboard deployment and simulation use-cases.
• Design, implement, and scale state-of-the-art distillation and efficiency pipelines, which may include:
  • Distillation for generative models (diffusion, autoregressive, flow-matching, video models)
  • Quantization-aware training (QAT) and post-training quantization (PTQ)
  • Knowledge distillation (feature-level, response-based, and relation-based)
  • Structured and unstructured pruning and sparsification
  • Low-rank factorization and efficient architecture design
  • Speculative decoding and other inference-time efficiency techniques
• Collaborate closely with ML Platform, Infrastructure, Onboard, Autonomy, and Simulation teams to integrate compressed models into production pipelines and meet latency, memory, and throughput targets across deployment contexts.
• Define rigorous benchmarks and evaluation frameworks to characterize efficiency vs. quality trade-offs across models and hardware targets.
• Mentor and guide researchers and engineers working in the distillation and model efficiency space, setting a high technical bar and fostering a culture of rigorous experimentation.
• Champion best practices for model compression across the organization; disseminate knowledge through internal design reviews, documentation, and technical talks.
• Stay at the cutting edge of model efficiency research; contribute to the broader scientific community through publications and open-source contributions.

• Deep distillation expertise: You have extensive hands-on experience designing and implementing distillation, quantization, pruning, and model compression techniques for large-scale neural networks, with demonstrated impact in production settings.
• Strong research and engineering foundation: A Bachelor's or Master's degree in Machine Learning, Computer Vision, Robotics, or a related field, or equivalent industry experience; relevant hands-on experience in model distillation and efficiency is what matters most. Expert Python and PyTorch (or JAX) skills with experience in large-scale distributed training.
• Technical leadership: You have a proven track record of setting technical direction and driving projects from conception to production. You inspire and elevate those around you through deep technical expertise and mentorship.
• Cross-functional collaboration: You have experience working closely with infrastructure, platform, and autonomy teams to deploy compressed models under real engineering constraints.
• Clear communicator: You can communicate complex technical trade-offs clearly to diverse audiences and drive alignment across research and engineering teams.

• Experience with hardware-aware optimization (TensorRT, ONNX, custom CUDA kernels, hardware-specific quantization).
• Publications at top-tier ML/CV venues (NeurIPS, ICML, CVPR, ICLR, ECCV) in model compression, efficient deep learning, or related areas.
• Experience distilling large generative models (diffusion models, LLMs, VLMs, or video models).
• Background in autonomous vehicles or robotics.