Principal Machine Learning Engineer - Autonomy

Wayve•Sunnyvale, CA

9d•Hybrid

About The Position

At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. About us Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact. Make Wayve the experience that defines your career! The role As a Principal Learning Engineer within the Autonomy team, you’ll lead critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalization, comfort, and collaboration. You’ll design and deliver ML-driven behaviors that scale from assisted to autonomous driving. Your work will span across model architecture, data pipelines, evaluation frameworks, and real-world deployment. You’ll collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable, and ready for production. What You’ll Be Working On

Requirements

7+ years (Staff) or 10+ years (Principal) years in ML engineering, with a strong 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.

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