Senior ML Engineer—Vision & Scene Understanding

Wayve; Sacramento (CA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Wayve in Sunnyvale is seeking an experienced ML Engineer to build and deploy offline scene-understanding models for evaluation and validation. You will work on transformer-based, multimodal architectures and offline models using larger compute budgets.

The role involves benchmarking, improving generalisation across vehicle platforms and geographies, collaborating with data and simulation teams, and mentoring others while upholding strong ML engineering practices.

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 or foundation models; understand data, loss, and representation trade-offs.
• Proficient in Python and PyTorch; comfortable with large-scale training.
• Experience in 3D scene understanding, offline modelling, autonomous vehicles, or robotics is valuable.

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 your models, diagnose failure modes, and use error analysis to guide iteration.
• Define quality bars and statistically 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 machine-learning and engineering practices.

Skills

ML engineering

CV/Transformer/Multimodal

Python & PyTorch

Fine-tuning foundation models

Large-scale training

Job description

Wayve in Sunnyvale is seeking an experienced ML Engineer to build and deploy offline scene-understanding models for evaluation and validation. You will work on transformer-based, multimodal architectures and offline models using larger compute budgets.

The role involves benchmarking, improving generalisation across vehicle platforms and geographies, collaborating with data and simulation teams, and mentoring others while upholding strong ML engineering practices.