Senior / Staff Machine Learning Engineer at Wayve

Department: AV Engineering

Compensation

$311,850 – $389,400/yr

Setup: Hybrid
Location: Sunnyvale, California
Type: Full-time
Level: staff+
Posted: Sep 10, 2026

Description

Wayve is hiring a Senior / Staff Machine Learning Engineer for its AV Core organisation to advance reinforcement learning for end-to-end driving models. The role owns reinforcement learning methods, develops post-behavior-cloning optimization techniques, improves reward models and training workflows, evaluates policies across simulation and on-road tests, and productionizes successful methods. It requires strong reinforcement learning and sequential decision-making expertise, Python and PyTorch proficiency, and senior-level technical leadership. The position is full-time and hybrid in Sunnyvale, with an estimated salary of $311,850 to $389,400 plus equity.

Required Skills

Reinforcement learning
Python
PyTorch
Behavior cloning
Reward modeling
C++
CUDA

Additional Information

Required Previous Experiences: Strong track record developing and experimentally validating reinforcement learning or related sequential decision-making methods on complex, high-dimensional problems
Hands-on experience with behavior cloning, reinforcement learning, or related methods
Proficiency in Python and PyTorch with experience building reliable machine learning training and evaluation systems
Senior-level ownership and collaboration across research and engineering boundaries
Preferred Qualifications: Experience with offline reinforcement learning, imitation learning, reward modeling, preference learning, or post-training of large neural policies
Experience in autonomous vehicles, robotics, control, or safety-critical physical systems
Experience with closed-loop simulation, off-policy evaluation, uncertainty or calibration, and evaluation under rare or shifted conditions
Experience training multimodal, transformer-based, or generative policy models at scale
Proficiency in C++, CUDA, distributed training, or performance optimization for production machine learning systems