Distillation Lead

Distillation Lead

Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.

Waabi's Physical AI platform is powered by state of the art ML models which must be deployed efficiently across diverse use-cases, from onboard vehicle inference to large-scale simulation. As the Distillation Lead, you will own the strategy and execution for distillation across Waabi's AI stack, ensuring our most capable models run efficiently in every deployment context. You will partner closely with ML Platform, Infrastructure, Onboard Autonomy, and Simulation teams to deliver compressed models that meet the performance requirements of both real-time onboard systems and high-throughput simulation pipelines.

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.

The US yearly salary range for this role is: $195,000 - $286,000 USD in addition to competitive perks & benefits. Waabi (US) Inc.'s yearly salary ranges are determined based on several factors in accordance with the Company's compensation practices. The salary base range is reflective of the minimum and maximum target for new hire salaries for the position across all US locations. Note: The Company provides additional compensation for employees in this role, including equity incentive awards and an annual performance bonus.

Perks/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.

- As we grow, this list continues to evolve!

Waabi is a technology start-up building technologies to transform the way the world moves. Join our talented team to be a part of the future and to make an impact!

Waabi is an equal opportunity employer. We celebrate diversity and are committed to creating a supportive, inclusive, and accessible workplace for all our employees. We seek applicants of all backgrounds and identities, across race, color, ethnicity, national origin or ancestry, age, citizenship, religion, sex, sexual orientation, gender identity or expression, military or veteran status, marital status, pregnancy or parental status, caregiver status, disability, or any other characteristic protected by law. We make workplace accommodations for qualified individuals with disabilities as required by applicable law. If reasonable accommodation is needed to participate in the job application or interview process please let our recruiting team know.