Applied Science Internship

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!

About The Role

Our team is seeking a talented Machine Learning Intern to join us for a period of 3-6 months and propel our ambitious research in embodied foundation models forward.

We're not just another team; we're a dynamic blend of Applied Scientists, Machine Learning Engineers, and Software Engineers united together to apply state-of-the-art research to the road. From pioneering advancements in large-scale, multi-modal embodied Foundation Models, Offline Reinforcement Learning (RL) and Reinforcement Learning from Human Feedback (RLHF), our projects are designed to dramatically enhance our capabilities in embodied AI. But it's not just about what we do—it's how we do it. We believe in the power of cross-functional collaboration, rigour in engineering, and a relentless pursuit of innovation.

In this role, you might:

• Contribute to the Wayve Foundation Model, including large scale pretraining, posttraining, leveraging 3D priors or improving spatial understanding.
• Help us efficiently train models with large-scale data and evaluate performance on open (and closed) datasets/benchmarks.
• Publish your work as an intern at a top tier conference (e.g., CVPR, ICCV, CoRL, NeurIPS, CoLM, RSS, ICRA, among others).
• Develop tools for data visualisation, understanding, and exploration.
  
  

You’d be a great match for this role if:

• You have previous experience in world models, vision-language models, large language models, natural language processing, computer vision, 3D priors, SLAM, or robotics.
• You have solid software engineering fundamentals, especially in Python
• You have previously used PyTorch or a similar library for deep learning (e.g. Tensorflow, JAX). Experience with distributed training a plus.
• You are interested in using large scale datasets to improve embodied AI.
  
  

Essentials:

• You are currently pursuing a graduate degree in a Computer Science, Machine Learning, Robotics, or related technical field.
• You are proficient in at least one backend/systems programming language (e.g. Python, Ruby, Java, etc).
  
  

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.

For more information visit Careers at Wayve.

To learn more about what drives us, visit Values at Wayve

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.