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Distillation Lead

Waabi· Software – Autonomy & Algorithms
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Hybrid (San Francisco, CA), Hybrid (Toronto, ON), Hybrid (Pittsburgh, PA), Remote$195k - $286kFull-timePosted 5mo ago

About the role

Waabi’s Distillation Lead owns the strategy and execution for distillation across Waabi's AI stack, ensuring models run efficiently in onboard deployment and high-throughput simulation, collaborating with ML Platform, Infrastructure, Onboard Autonomy, and Simulation teams.

Responsibilities

  • 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: 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: Bachelor’s or Master’s degree in Machine Learning, Computer Vision, Robotics, or related field, or equivalent industry experience; expert Python and PyTorch (or JAX) skills with experience in large-scale distributed training.
  • Technical leadership: proven track record of setting technical direction and driving projects from conception to production.
  • Cross-functional collaboration: experience working closely with infrastructure, platform, and autonomy teams to deploy compressed models under real engineering constraints.
  • Clear communicator: ability to convey complex technical trade-offs clearly to diverse audiences and drive alignment across research and engineering teams.

Skills

Required:Model distillationQuantization (QAT/PTQ)Pruning and sparsificationKnowledge distillationPythonPyTorch or JAX
Nice to have:Low-rank factorizationInference-time efficiency techniques

Benefits

  • Competitive compensation and equity awards.
  • Health and Wellness benefits encompassing Medical, Dental and Vision coverage (for full-time employees only).
  • Unlimited Vacation.
  • Flexible hours and Work from Home support.
  • Daily drinks, snacks and catered meals (when in office).
  • Regularly scheduled team building activities and social events both on-site, off-site & virtually.
Last updated Oct 21, 2026. Interested? Apply on the company site.

About Waabi

pioneering Physical AI, starting with autonomous trucks.

Waabi is pioneering Physical AI, starting with autonomous trucks. We developed a next-generation approach leveraging an end-to-end interpretable and verifiable AI model that’s powered by the industry's most realistic neural simulator. This dramatically reduces the development time and resources needed to bring self-driving vehicles to public roads — safely and at scale.

Autonomous trucksPhysical AIneural simulatorAI modelself-driving vehicles
Company profileWebsite

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