Applied Scientist, Wayve
World Modeling
2-5 days a week in office
Autonomous mobility driven by AI
Be an early applicant
Autonomous mobility driven by AI
201-500 employees
Be an early applicant
2-5 days a week in office
201-500 employees
Company mission
To reimagine autonomous mobility through embodied intelligence.
Role
Who you are
- 2+ years of experience in ML engineering or applied research roles
- Deep knowledge of generative modelling (e.g., auto-regressive, diffusion, or VAEs)
- Experience working with high-dimensional temporal or spatial-temporal data (e.g., video, multi-sensor fusion)
- Strong Python and PyTorch engineering fundamentals, and experience building research-grade production tools
- Strong publication record or contributions to open-source ML tooling
- Ability to work collaboratively in a fast-paced, innovative, interdisciplinary team environment
- Experience in AVs, robotics, simulation, or other embodied AI domains
- Experience working with synthetic-to-real transfer
What the job involves
- Science is the team that is advancing our end-to-end autonomous driving research
- The team’s mission is to accelerate our journey to AV2.0 and ensure the future success of Wayve by incubating and investing in new ideas that have the potential to become game-changing technological advances for the company
- This role would sit within Science focusing on unlocking disruptive innovation that solves self-driving
- We believe the next big leap in autonomy won’t come from collecting more real-world data—it’ll come from learning to simulate the world with unprecedented fidelity and generalization
- As an Applied Scientist in the Science team, you’ll play a key role in developing next-generation world models and planners that can simulate complex, diverse, and temporally consistent driving environments
- These generative simulation models (like GAIA-2) will power faster training, broader testing, and scalable deployment—even in areas and scenarios we’ve never driven in before
- You’ll work at the intersection of machine learning research, multi-modal modeling, and real-world deployment—tackling questions like:
- How can we deploy AVs in a new geography without collecting any real-world data?
- Can synthetically generated environments fully replace physical testing and data collection?
- Invent and iterate on generative world models (e.g., diffusion, transformer-based, or hybrid) to simulate dynamic, interactive driving scenarios with high realism and controllability
- Train large-scale temporal models on multi-modal data (video, LiDAR, vehicle telemetry), learning representations of complex real-world dynamics
- Design experiments to understand model generalization, scaling behavior, and performance trade-offs between synthetic and real data
- Define and track metrics and benchmarks for long-horizon prediction, scene fidelity, and planner integration
- Challenge assumptions and drive innovation: propose bold ideas, conduct ablation studies, and question conventional approaches to training and evaluation
- Contribute to the broader research community through publications, talks, and open-source contributions
Salary benchmarks
Our take
Wayve is developing artificial intelligence (AI) that teaches cars to drive autonomously using reinforcement learning, simulation, and computer vision. Wayve’s core premise is that the big breakthrough in self-driving cars will come from better AI brains rather than more sensors or “hand-coded” rules which it believes are highly restrictive and not at all scalable.
The company said that it trains its autonomous driving system using simulated environments and then transfers that knowledge into the real world, where it emulates how humans adapt to conditions in real time. It ultimately relies on end-to-end deep learning AI rather than hard-engineered AI. This is one of the world's hardest problems to solve, but Wayve has made an exciting start and is taking a very different approach to competitors like Uber and Waymo, who are relying more on sensors.
Following a few years of innovative breakthroughs, Wayve now has backing from high-profile investors such as Microsoft and angels, including Uber's chief scientist Zoubin Ghahramani and Pieter Abbeel, a UC Berkeley robotics professor and pioneer of deep reinforcement learning. The company's strategic partnerships with outfits like Asda and Ocado to test-run autonomous deliveries, as well as publicity through the Minister is a show of confidence in the future of Wayve's solution to autonomous driving.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 7 rounds)
May 2024
$1.1bn
SERIES C
Jan 2022
$200m
SERIES B
Total funding: $1.3bn
Company benefits
- Learning budget
- In-house chef
- Flexible Working
- Private health insurance and therapy
- Workplace nursery scheme
- Onsite bar
- Large social budgets
- Enhanced parental leave
Company HQ
London, UK
Leadership
Alex Kendall
(CEO)Has a PhD from Cambridge in Computer Vision & Robotics. Previously Research Engineer at Skydio and Advisor to Scape Technologies
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