Wayve
AI
MachineLearningEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Machine Learning Engineer at Wayve. Skills: Deep learning, Python, Autonomous driving, ML systems. Develop end-to-end driving models. Improve driving models”
What You'll Achieve.
Enhancing the usability and safety of automated driving systems; Accelerating the transition from assisted to automated driving; Deliver ML-driven behaviors that scale; Build systems that are performant, adaptable, and ready for production; Ensure product readiness; Drive scenario diversity; Drive coverage; Drive feature-specific development; Deliver impact
Industry & Context.
Embrace uncertainty; Lean into complex challenges
What They're Looking For.
Must Have
Extensive and proven track record of shipping deep learning systems to production, Expert in deep learning (esp. sequential models, control, planning, or perception), Proficient in Python and other relevant languages (e. g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices, Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components, Ability to lead technical initiatives across teams, drive alignment, and mentor engineers
Nice to Have
Prior work in autonomous driving, imitation learning, or trajectory prediction, Familiarity with personalization, human behavior modeling, or driver intent inference, Experience integrating ML systems into production hardware or multi-agent simulation
What You'll Do.
Develop end-to-end driving models
Improve driving models
Lead personalized driving projects
Lead collaborative driving projects
Build evaluation pipelines
Curate real-world data
Curate synthetic data
Influence architecture choices
Influence training methodologies
Influence deployment pathways
Collaborate cross-functionally
Ensure iteration velocity
Mentor senior engineers
Shape technical direction
How You'll Work.
Team & Collaboration
Collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams; Collaborate cross-functionally across various teams; Drive alignment across teams; Back each other to deliver impact
Process & Methodology
Lead technical initiatives
Full Job Description
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 an ML Engineer within the Application Engineering team, you’ll lead critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalisation, comfort, and collaboration. You’ll design and deliver ML-driven behaviors that scale from assisted to autonomous driving. Your work will span across model architecture, data pipelines, evaluation frameworks, and real-world deployment. You’ll collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable, and ready for production. Responsibilities: Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization. Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment. Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance an
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