Senior Machine Learning Engineer, Vision Models

Wayve • Sunnyvale, CA

21h • Hybrid

About The Position

The Measurement team within Wayve’s AI Evaluation organization builds and qualifies offline scene-understanding models used to measure driving performance after on-road runs and in simulation. The role focuses on developing credible, fleet-scale evidence by using offline modeling’s greater compute capacity, larger models, and access to past and future temporal context.

The engineer will build, train, and fine-tune scene-understanding models; adapt on-vehicle architectures and Wayve foundation models for offline use; and improve accuracy and generalization across vehicle platforms, geographies, and driving conditions. The work includes benchmarking models, diagnosing failure modes, guiding iteration through error analysis, and defining quality bars and statistically credible evidence for validation pipelines and safety cases.

The role involves transformer-based, multimodal, and vision-language models for detection, segmentation, classification, and scene understanding; automated benchmarks for coverage, rare-event mining, and driving-behavior measurement; and fleet-scale camera, lidar, and simulation datasets. The models and evidence support development decisions, validation pipelines, customer deliverables, and safety cases. The engineer will collaborate with on-vehicle modeling, evaluation, data curation, and simulation teams, mentor others, and contribute to strong machine-learning and engineering practices.

The position is based in Sunnyvale and follows a hybrid working model combining in-person collaboration with focused remote work. Wayve describes its environment as one where ways of working are still being developed, offering ownership to people who want to help shape them.

Requirements

• At least 4 years of experience in machine-learning engineering.
• Experience training and shipping deep-learning models in production.
• Hands-on experience with modern computer-vision, transformer, multimodal, or vision-language model architectures.
• Experience adapting or fine-tuning large pretrained or foundation models, with understanding of data, loss, and representation trade-offs.
• Proficiency in Python and PyTorch or a similar framework.
• Comfort working with large-scale training.
• Ability to take ambiguous modeling problems from scoping through to a working, measurable solution.

Nice To Haves

• Experience in 3D scene understanding.
• Experience in offline modeling or auto-labeling.
• Experience with temporal or world models.
• Experience in autonomous vehicles or robotics.
• Experience with distributed training.

Responsibilities

• Build, train, and fine-tune scene-understanding models for offline measurement.
• Adapt on-vehicle architectures and Wayve foundation models for offline use.
• Improve model accuracy and generalization across vehicle platforms, geographies, and driving conditions.
• Benchmark 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 modeling, evaluation, data curation, and simulation teams.
• Develop offline models using higher compute budgets, larger capacity, and bidirectional temporal context.
• Contribute to automated benchmarks that assess coverage, mine rare events, and measure driving behavior.
• Mentor others and contribute to strong machine-learning and engineering practices.

Benefits

• Meaningful equity.
• Relocation support and visa sponsorship where applicable.
• Hybrid working and access to vehicle workshops and labs.
• Learning and development support.
• Comprehensive location-dependent health, family, retirement, and wellbeing benefits.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed