Tech Lead, Wayve Labs

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 Wayve Labs team is a global group of leading scientists and engineers advancing the frontiers of autonomous driving through Embodied AI. We do this by unlocking breakthrough capabilities through cutting-edge research, enabling rapid integration of new technologies into production and disrupting ourselves, before someone else does.

We’re looking for Applied Scientists to join Wayve’s Science team and help build the next generation of AI systems for autonomous driving. You’ll work at the intersection of machine learning, simulation, robotics, and real-world deployment, contributing to core innovations that push the boundaries of what embodied AI can do.

• →Design and train large-scale embodied foundation models, advancing architectures in attention, mixture-of-experts, and distributed training.
  
  
• →Develop world models and planners (e.g., diffusion, transformer-based, or hybrid generative approaches) to simulate diverse and temporally consistent driving environments.
  
  
• →Innovate in reinforcement learning and reward modeling, building safe, efficient, and scalable reward frameworks integrated with real-world and synthetic data.
  
  
• →Conduct empirical research on scaling laws, generalisation, and synthetic-to-real transfer for autonomous systems.
  
  
• →Define benchmarks and metrics for long-horizon prediction, scene fidelity, planner integration, and driving performance.
  
  

• PhD, Master’s degree, or equivalent experience in Machine Learning, Computer Vision, Robotics, or related fields
  
  
• Applicants should have a minimum of 4 years’ experience in applied ML research or engineering. We are hiring for this level and above, so candidates with deeper expertise are encouraged to apply.
  
  
• Deep expertise in one or more of the following:
  
  
• Foundation model development (e.g., Transformers, MoE, large-scale training)
• Generative world modeling (e.g., autoregressive, diffusion, hybrid models)
• Reinforcement learning, including offline RL, RLHF, reward modeling
• Spatial AI, including transformer-based techniques for SFM/SLAM, depth estimation and multi-view geometry, making use of multimodal (RGB, LIDAR, RADAR, IMU) sensors.
• Other related areas of Machine Learning.
  
  
• Strong programming skills in Python and experience with PyTorch or equivalent ML frameworks
  
  
• Track record of publishing at top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, CoRL)
  
  
• Strong problem-solving ability, collaboration mindset, and ability to thrive in a fast-paced, interdisciplinary team
  
  

• Experience in autonomous driving, robotics, or simulation
  
  
• Familiarity with training at scale (e.g., FSDP, DeepSpeed, JAX)
  
  
• Knowledge of synthetic-to-real transfer or data-efficient learning
  
  
• Contributions to open-source ML tools or research infrastructure