Staff / Senior Machine Learning Engineer, AV Core

📋 Job Overview

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

🏢 About Wayve

Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines.

Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility.

🎯 The Role

As a Senior/Staff Machine Learning Engineer in Wayve's AV Core organization, you will lead the technical direction and delivery of learned emergency manoeuvre prediction and collision detection models. Emergency manoeuvres are rare, high-consequence events that place unusual demands on data, modelling, and validation. You'll take the programme from problem definition through modelling, evaluation, integration, and evidence for deployment.

✅ Key Responsibilities

• Drive Core Model Safety roadmap themes, owning the full lifecycle from research to offline/online experiments to technology transfer.
• Train and deploy end-to-end AV 2.0 models for emergency manoeuvre prediction and collision detection on our global fleet, using large-scale, diverse data to validate capabilities and improve generalisation across vehicles, markets, and driving conditions.
• Collaborate on online occupancy models for geometric and semantic perception.
• Build high-value open-loop and closed-loop evaluations for core capabilities and representation learning.
• Align priorities and learn from the organisation — with AV Core, Evaluation, and Product Engineering on roadmaps and failure modes; from fleet, simulation, and product feedback; and through mentoring others on the team.
• Maintain awareness of the wider business context — division and company priorities, near-term product programmes, and how Core Model Safety work enables them.

🧠 Required Qualifications

• You have a strong track record of ML engineering, including pathfinding in ambiguous problems — from scoping and evals to establishing a direction (and knowledge transfer) for others to build on.
• You have hands-on experience with ML systems deployed in the real world.
• You're proficient in Python and PyTorch, with strong software engineering practices and hands-on experience building reliable machine learning training and evaluation systems.
• You have excellent experimental judgement: able to turn an ambiguous behavioral problem into falsifiable hypotheses, useful metrics, disciplined ablations, and clear technical decisions.
• You bring senior-level ownership and collaboration: able to lead a substantial technical area, work across research and engineering boundaries, and bring others along through clear written and verbal communication.

⭐ Desirable Experience

• Prior experience in autonomous vehicles or robotics with hands-on deployment and closed-loop validation on physical systems.
• Experience in 3D scene understanding and representation learning for geometric and semantic perception, large-scale semantic enrichments.
• Experience mining, generating, or evaluating rare events using simulation and fleet or heterogeneous real-world data.
• Experience with transformer-based and multimodal architectures, including vision-language models (VLM), vision-language-action models (VLA), or equivalent.
• Proficiency in C++, CUDA, distributed training, or performance optimization for production machine learning systems.

🎁 Benefits

Compensation: $311,850 – $389,400

🛂 Visa & Eligibility

Not specified