Applied Scientist, Reinforcement Learning & Reward Modeling

Wayve; Vancouver; On-site; CAD 90,000 - 130,000; Full time

Job summary

Wayve is seeking an experienced Applied Scientist to enhance their AI driving technology. The role involves designing reinforcement learning models and collaborating with top researchers to advance autonomous systems. Ideal candidates will have expertise in machine learning, strong programming skills, and a passion for innovation in self-driving technology.

Benefits

Competitive compensation and stock options

Help with relocation and visa sponsorship

Flexible working hours

Lunch and team socials

Qualifications

• Proven expertise in reinforcement learning and machine learning.
• Strong programming skills in Python.
• Experience with simulation environments and real-world data.

Responsibilities

• Design and optimize reward models for autonomous vehicles.
• Work with multidisciplinary teams on integration of models.
• Define a data strategy for real and synthetic data.

Skills

Reinforcement Learning

Machine Learning

Python

Problem-Solving

Collaboration

Tools

PyTorch

Job description

At Wayve we're 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.

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.

At Wayve, 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

We're looking for an experienced Applied Scientist with expertise in Reinforcement Learning and Reward Modelling to advance our training and evaluation frameworks contributing significantly to the creation of safe and reliable AI driving technology. The ideal candidate has a deep understanding of reinforcement learning, machine learning, and behavioural modelling, combined with a drive to innovate in the autonomous driving space.

In this role, you will be at the forefront of designing and optimizing reward and reinforcement learning models that are powerful and resource-efficient, tailored for the unique demands of embodied AI and autonomous systems. Your work will involve but not limited to:

• Design, develop, and refine reward models that align with safe and efficient driving objectives for autonomous vehicles.
• Work closely with multidisciplinary teams to integrate reward models with real-world data and simulation frameworks.
• Define a data strategy that includes efficient use of real and synthetic data, annotations, and active learning.
• Design experiments to evaluate reward structures in diverse driving scenarios and identify areas for improvement.
• Collaborate with world-class researchers and engineers to push the boundaries of AI, contributing significantly to the evolution of autonomous driving technology

About you

In order to set you up for success as an Applied Scientist at Wayve, we’re looking for the following skills and experience.

Must haves:

• Proven expertise in reinforcement learning, including in areas like offline RL, reward modeling, RLHF, DPO, GPRO, as well as experience with machine learning.
• Strong programming skills in Python and experience with machine learning libraries such as PyTorch.
• Experience in working with simulation environments and real-world data for model validation and performance benchmarking.
• Demonstrated ability to publish research and present findings to both technical and non-technical audiences at top tier conferences.
• Excellent problem-solving skills and the ability to work independently as well as in a team environment.
• Demonstrated ability to work collaboratively in a fast-paced, innovative, interdisciplinary team environment.
• Track record of publications at top-tier conferences like NeurIPS, CVPR, ICRA, ICLR, CoRL etc.
• Familiarity with self-driving technologies, sensor data processing, and real-time decision-making algorithms.
• Experience with large-scale machine learning systems, distributed training and deploying models in production environments.

What we offer:

• A position to shape the future of autonomous driving, and thus to tackle one of the biggest challenges of our time
• Immersion in a team of world-class researchers, engineers and entrepreneurs
• Competitive compensation and stock options
• Help relocating/traveling, with visa sponsorship
• Flexible working hours - we trust you to do your job well, at times that suit you and your team
• Lunch and team socials

#LI-MB1

We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.

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.

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