Staff ML Engineer: Emergency Trajectory (Hybrid, Equity)

Wayve; Sunnyvale (CA); Hybrid; USD 336,000 - 370,000; Full time

Benefits offered by this job

Equity package

Hybrid working policy

Job summary

Wayve is hiring a Staff Machine Learning Engineer in Sunnyvale for the AV Core org to lead the learned emergency trajectory model from problem definition through modelling, evaluation, integration and deployment. You will collaborate across simulation, evaluation, safety, and product engineering to define the operating envelope and evidence base.

The role demands senior ML leadership, hands-on trajectory generation experience, and strong Python/PyTorch engineering skills.

Qualifications

• Staff-level technical leadership across ambiguous ML programmes.
• Deep expertise in learned trajectory-generation for embodied systems.
• Hands-on with behaviour cloning or RL and robustness.
• ML engineering in Python and PyTorch with reproducible pipelines.
• Strong judgement for safety-critical trade-offs and explicit evidence.

Responsibilities

• Set the technical strategy and roadmap for the emergency trajectory model.
• Design and train trajectory-generating policies using evidence-based methods.
• Build a data strategy for rare emergency cases using fleet data and simulation.
• Create open-loop and closed-loop evaluations for safety metrics.
• Lead integration into the shared driving stack and mentor teams.

Skills

Staff-level leadership

Trajectory models

Behaviour cloning

Reinforcement learning

Python

PyTorch

Safety trade-offs

Tools

C++

CUDA

Systems optimisation

Job description

Wayve is hiring a Staff Machine Learning Engineer in Sunnyvale for the AV Core org to lead the learned emergency trajectory model from problem definition through modelling, evaluation, integration and deployment. You will collaborate across simulation, evaluation, safety, and product engineering to define the operating envelope and evidence base.

The role demands senior ML leadership, hands-on trajectory generation experience, and strong Python/PyTorch engineering skills.