Senior ML Engineer – Vision & Offline Models (Equity)

Wayve; Sunnyvale (CA); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Hybrid working

Relocation support and visa sponsoring

Learning and development support

Location-dependent health, family, and

wellbeing benefits

Job summary

Wayve is hiring for an ML Engineer on the Measurement team in Sunnyvale. You will build and refine scene-understanding models for offline measurement, adapt large foundation models for offline deployment, and improve generalisation across diverse driving conditions and platforms.

The role involves benchmarking, error analysis, and collaboration with multiple teams to drive credible validation and safety evidence.

Qualifications

• 4+ years in ML engineering and shipped deep-learning models in production.
• Hands-on experience with modern computer-vision, transformer, multimodal, or VLM architectures.
• Adapted or fine-tuned large pretrained/foundation models.

Responsibilities

• Build, train, and fine-tune scene-understanding models for offline measurement.
• Adapt on-vehicle architectures and Wayve foundation models for offline use.
• Improve accuracy and generalisation across vehicle platforms and driving conditions.
• Benchmark models, diagnose failure modes, and guide iteration.
• Define quality bars and evidence for validation pipelines and safety cases.
• Collaborate with on-vehicle modelling, evaluation, data curation, and simulation teams.
• Mentor others and contribute to strong ML and engineering practices.

Skills

4+ years ML engineering

Vision-language models

Foundation models adaptation

Python

PyTorch

3D scene understanding

Tools

PyTorch

Job description

Wayve is hiring for an ML Engineer on the Measurement team in Sunnyvale. You will build and refine scene-understanding models for offline measurement, adapt large foundation models for offline deployment, and improve generalisation across diverse driving conditions and platforms.

The role involves benchmarking, error analysis, and collaboration with multiple teams to drive credible validation and safety evidence.