Principal Machine Learning Engineer, App SW

WayveSunnyvale, CA
Hybrid

About The Position

At Wayve, we are developing Embodied AI technology to enable vehicles to perceive, understand, and navigate complex environments, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward with intelligent, mapless, and hardware-agnostic AI products. We are a fast-paced company that embraces uncertainty and complex challenges to unlock groundbreaking solutions, aiming high while staying humble. Your contributions matter in our diverse, inclusive, and respectful culture where we back each other to deliver impact. Make Wayve the experience that defines your career!

Requirements

  • Extensive and proven track record of shipping deep learning systems to production.
  • Expert in deep learning (esp. sequential models, control, planning, or perception).
  • Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices.
  • Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components.
  • Ability to lead technical initiatives across teams, drive alignment, and mentor engineers.

Nice To Haves

  • Prior work in autonomous driving, imitation learning, or trajectory prediction.
  • Familiarity with personalization, human behavior modeling, or driver intent inference.
  • Experience integrating ML systems into production hardware or multi-agent simulation.

Responsibilities

  • Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization.
  • Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment.
  • Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness.
  • Curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development.
  • Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems.
  • Collaborate cross-functionally across various teams to ensure integration and iteration velocity.
  • Mentor senior engineers and shape the long-term technical direction across Autonomy.

Benefits

  • Competitive equity package
  • Hybrid working policy
  • Core working hours
  • Inclusive interview experience with accommodations available
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