Staff Machine Learning Engineer, Vision Models at Wayve

Department: Simulation, Evaluation, Validation

Compensation

$370,040 – $407,330/yr

Setup: Hybrid
Location: Sunnyvale, California
Type: Full-time
Level: staff+
Posted: Jun 30, 2026

Description

Wayve is hiring a Staff Machine Learning Engineer for its Measurement team in AI Evaluation, based in the Sunnyvale office. The role develops, trains, and fine-tunes computer vision and scene-understanding models for offline measurement of Wayve Driver performance, adapts on-vehicle and foundation models, improves accuracy and generalisation across vehicles, geographies, and conditions, benchmarks models, and produces statistically defensible evidence for validation pipelines and safety cases. The position requires at least five years of machine learning engineering experience, hands-on computer vision and foundation-model experience, Python and PyTorch proficiency, and staff-level technical leadership. It is a full-time hybrid role with an estimated salary of $370,040 to $407,330 plus competitive equity.

Required Skills

Computer vision
Scene understanding
PyTorch
Foundation models
Python
Large-scale training
3D scene understanding

Additional Information

Required Previous Experiences: 5+ years of machine learning engineering experience, including training and shipping deep learning models in production
Hands-on experience training modern computer vision models, including transformer-based and multimodal or VLM architectures, on camera and/or lidar data
Experience adapting or fine-tuning large pretrained or foundation models and training shared representations across multiple tasks
Proficiency in Python and PyTorch with a foundation in software engineering and large-scale training
Staff-level technical leadership in setting direction and leading cross-functional work without formal line management
Preferred Qualifications: Experience in 3D scene understanding and representation learning for geometric and semantic perception
Experience with offboard or offline modelling, including auto-labelling, model distillation, temporal or world models
Prior experience in autonomous vehicles or robotics with hands-on deployment and closed-loop validation on physical systems
Experience with fleet-scale data and large-scale distributed training infrastructure