Staff Machine Learning Engineer, Emergency Trajectory Models

The role 

As a Staff Machine Learning Engineer in Wayve's AV Core organization, you will lead the technical direction and delivery of a learned emergency trajectory model for low-frequency, high-consequence maneuvers such as evasive steering and emergency braking. You will take the programme from problem definition through modelling, evaluation, integration, and evidence for deployment.

Emergency maneuvers are rare, high-consequence events that place unusual demands on data, modelling, and validation. The hard problem is not simply to train another trajectory head: it is to define the operating envelope of a specialist model, what evidence shows that it improves outcomes without introducing new failure modes, and how it integrates with the general driving model and surrounding system. You will lead that work across AV Core and with partners across simulation, evaluation, safety, and product engineering.

Key responsibilities

• Set the technical strategy and roadmap for the emergency trajectory model, including its behavioral scope, operating envelope, system interfaces, and measurable acceptance criteria.
• Design and train trajectory-generating policies using the methods best supported by evidence, including behaviour cloning, reinforcement learning, or other sequential decision-making approaches.
• Build a data strategy for rare emergency cases, combining fleet data, targeted mining, simulation, augmentation, and reweighting while controlling coverage gaps and unintended behavior.
• Create rigorous open-loop and closed-loop evaluations for collision avoidance, evasive steering, emergency braking, recovery, robustness, latency, and regressions in nominal driving.
• Lead integration into the shared driving stack, align technical decisions across teams, and raise the bar through architecture reviews, mentoring, and clear communication of risks, trade-offs, and evidence.

About you  

In order to set you up for success as a Staff Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.  

Essential 

• A track record of staff-level technical leadership: setting direction for ambiguous machine learning programmes, aligning multiple teams, and carrying work from research through production deployment.
• Deep expertise developing learned trajectory-generation or policy models for embodied systems, including architecture design, objective design, training, and empirical validation.
• Hands-on experience with behaviour cloning, reinforcement learning, or related methods, including objective design, distribution shift, robustness, and closed-loop failure analysis.
• Strong machine learning engineering skills in Python and PyTorch, with experience building reproducible training and evaluation systems on large, heterogeneous datasets.
• Exceptional technical judgement and communication: able to make safety-relevant trade-offs explicit, define the evidence needed for decisions, and lead without relying on formal authority.

Desirable 

• Experience applying learned models in autonomous driving or robotics, with strong understanding of motion planning, vehicle dynamics, control, or collision avoidance.
• Experience with specialist, fallback, redundant, mixture-of-experts, or model-routing architectures and the interfaces used to select between them.
• Experience mining, generating, or evaluating rare events using simulation and fleet or real-world data.
• Experience deploying learned policies under real-time latency, reliability, and compute constraints; proficiency in C++, CUDA, or systems optimisation.
• Experience with multimodal, transformer-based, diffusion-based, or other generative trajectory or policy models.

This is a full-time role based in our office in Sunnyvale.  At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. The reasonably estimated salary for this role ranges from $336,400 to $370,300, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.