Staff Machine Learning Engineer, AV Core Sunnyvale, California USA

About us Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast-paced environment big problems ignite us-we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact. Make Wayve the experience that defines your career! 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 will take the programme from problem definition through modelling, evaluation, integration, and evidence for deployment. The Core Model Safety team builds foundational capabilities for assisted and automated driving - collision avoidance, model understanding, and robustness under failure. You will work in a focused, high-impact senior team with strong ownership, access to large-scale training and fleet data, and close partners in research, simulation, evaluation, and applied engineering. 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. About you In order to set you up for success as a Staff/Senior Machine Learning Engineer at Wayve, we're looking for the following skills and experience. Essential • 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. • Hands-on experience with ML systems deployed in the real world. • Proficiency in Python and PyTorch, with strong software engineering practices and hands-on experience building reliable machine learning training and evaluation systems. • Excellent experimental judgement: able to turn an ambiguous behavioral problem into falsifiable hypotheses, useful metrics, disciplined ablations, and clear technical decisions. • 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 • 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. 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 $311,850 to $389,400, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience. Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know. We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. Wayve is committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. For more information visit Careers at Wayve. To learn more about what drives us, visit Values at Wayve For US candidates only, please visit E-Verify Notice and Participation and Right to Work DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.