Distillation Lead

With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai

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.

Qualifications:

- 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.

Bonus:

- 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.