Staff Research Scientist Reinforce Learning

9 hours ago Lead London, UK Hybrid Full Time Ai Jobs by Wayve

Wayve Visit Wayve website

Wayve is a London-headquartered embodied-AI company developing and licensing mapless, vehicle-agnostic driving software for assisted, automated, and robotaxi applications.

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London, United Kingdom

Funding history

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Investors

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About Wayve

Wayve Technologies Ltd. develops the Wayve AI Driver, an end-to-end, data-trained software platform that runs on onboard vehicle compute and native sensors. It is designed for OEM integration across L1 driver assistance through L4 automated driving, without HD maps.

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Skills

DeepspeedFoundation ModelFsdpJaxMachine LearningPythonPytorchReinforcement LearningReward ModelingRoboticsSimulationSlamSpatial AiWorld Model

Candidate Availability

svg]:pointer-events-none [&>svg]:size-3 [a&]:hover:bg-accent [a&]:hover:text-accent-foreground border-sky-500/20 bg-sky-500/5 text-sky-200 transition-colors cursor-pointer hover:border-sky-400/50 hover:bg-sky-500/15 focus-visible:border-sky-400/50" title="London, United Kingdom">Hybrid

• London
• Required
  
  

Required and preferred rules are kept separate and reflect the wording in the original posting.

About The Role

You will develop world models, planners, reinforcement-learning and reward-modeling systems for realistic simulation. You will advance geometric foundation models and multimodal robotics learning, research scaling and sim-to-real transfer, and define evaluation frameworks for long-horizon prediction and driving performance.

Requirements

• 3+ years of experience developing and deploying machine-learning systems in real-world or production settings
• PhD, Master's degree, or equivalent experience in machine learning, computer vision, robotics, or a related field
• Expertise in embodied AI areas such as foundation models, generative world modeling, reinforcement learning, or spatial AI
• Publication record at top-tier conferences
• Python and PyTorch experience
• Experience with large-scale datasets and evaluation
  
  

Responsibilities

• Develop world models and planners for realistic, consistent simulation
• Advance reinforcement learning and reward modeling across real and synthetic data
• Develop geometric foundation models for 3D spatial understanding
• Enable cross-embodiment robotics using multimodal foundation models
• Conduct research on scaling laws, generalisation, and sim-to-real transfer
• Define and evolve evaluation frameworks and benchmarks
  
  

Benefits

• Equity
• Relocation support with visa sponsorship
• Flexible working hours
• Onsite chef
• Workplace nursery scheme
• Private health insurance
• Therapy
• Daily yoga
• Onsite bar
• Large social budgets
• Enhanced parental leave