Senior ML Engineer - Vision & Offline Scene Understanding

Wayve; California City (CA); Hybrid; USD 150,000 - 190,000; Full time

Benefits offered by this job

Equity

Relocation support

Hybrid work

Learning & development

Benefits

Job summary

Wayve is hiring for an ML engineer on the Measurement team within AI Evaluation in Sunnyvale. You will build offline scene-understanding models, adapt large foundation models for offline use, and push accuracy across diverse driving scenarios.

You will work with data curation, simulation, and on-vehicle teams, benchmarking and validating models for safety cases while mentoring colleagues and promoting robust engineering practices.

Qualifications

• 4+ years in ML engineering and have trained 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 or foundation models and understand data, loss, and representation trade-offs.

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, geographies, and driving conditions.
• Benchmark models, diagnose failure modes, and use error analysis to guide iteration.
• Define quality bars and credible 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

Python

PyTorch

Transformers

Multimodal/VLM

Tools

PyTorch

TensorFlow

Job description

Wayve is hiring for an ML engineer on the Measurement team within AI Evaluation in Sunnyvale. You will build offline scene-understanding models, adapt large foundation models for offline use, and push accuracy across diverse driving scenarios.

You will work with data curation, simulation, and on-vehicle teams, benchmarking and validating models for safety cases while mentoring colleagues and promoting robust engineering practices.