Senior Machine Learning Engineer, Foundation Models

As a Senior Machine Learning Engineer, Foundation Models at Wayve, your primary responsibilities revolve around developing advanced AI algorithms for autonomous vehicles. This includes applying model-free and model-based reinforcement learning, working with large language models, and engaging in offline reinforcement learning. You will also be involved in architecture design for our AI models, leveraging real-world driving data, and finding innovative solutions to teach our robots how to drive safely in complicated environments.

Candidates for the Senior Machine Learning Engineer, Foundation Models role at Wayve should possess at least 7+ years of experience in AI research, robotics, or deep learning. A thorough understanding of advanced algorithms such as reinforcement learning and behaviour cloning is essential. Familiarity with deep learning frameworks, particularly Python and PyTorch, as well as a PhD in a relevant field, is highly desirable. Experience in software engineering and automotive technology further enhances your eligibility.

Wayve is deeply committed to the professional development of its team members, including Senior Machine Learning Engineers. You'll have the opportunity to contribute to cutting-edge research and attend top-tier conferences like NeurIPS and CVPR, which nourish academic growth. Additionally, our culture emphasizes continuous learning and collaboration, allowing you to advance your skills while working on innovative AI solutions.

As a Senior Machine Learning Engineer at Wayve, you will engage in complex projects that drive the future of autonomous driving. This includes developing models for perception, prediction, planning, and control, all while working with diverse data sources and advanced algorithms. You'll also be part of a cross-disciplinary team that tackles the challenges of operating vehicles in dynamic and challenging environments, contributing directly to the evolution of our cutting-edge technology.

Teamwork is absolutely essential for the Senior Machine Learning Engineer, Foundation Models role at Wayve. Our interdisciplinary teams collaborate to solve complex challenges in the AI landscape, ensuring that all voices are heard and innovative ideas can flourish. As you work alongside fellow engineers and researchers, you'll find that sharing knowledge and learning from one another not only propels individual success but also drives the company's mission forward.

When answering this question, emphasize the specific problem you tackled, the approach you took, and the results achieved. Discuss the algorithms used, any challenges encountered, and how the project contributed to your growth as a machine learning engineer.

Share your methods for staying informed, such as reading research papers, attending conferences, or participating in online courses. Highlight your passion for the subject and your proactive approach toward integrating new knowledge into your work.

Discuss strategies such as policy evaluation, bootstrapping methods, and the importance of off-policy data. Demonstrate your understanding of these techniques and how they can be effectively deployed in real-world scenarios.

Outline your methodology for scaling, which may include optimizing algorithms, leveraging powerful hardware, and implementing efficient data pipelines. Discuss the trade-offs you might consider to maintain performance while achieving scalability.

Describe your analytical approach, focusing on methods used for data visualization, metrics interpretation, and adjustments based on performance insights. Emphasize the importance of refining models based on your findings to improve their capabilities.

Pick a couple of algorithms you are familiar with and discuss the context in which you implemented them, including the problem being solved and the outcomes achieved. This shows your hands-on experience and gives insight into your thought process.

Discuss your proficiency in Python and PyTorch, along with any other languages or frameworks you have utilized. Highlight specific projects where you successfully applied these tools to deliver high-quality code in machine learning and AI.

Using the STAR method (Situation, Task, Action, Result), outline a relevant experience where your collaborative efforts and problem-solving skills were crucial. Focus on your specific contributions and the positive outcomes resulting from effective teamwork.

Highlight practices such as code reviews, testing, and documentation. Discuss how adhering to these standards enhances collaboration and results in more robust, maintainable codebases, which is vital for collaborative environments like Wayve.

Express your alignment with Wayve’s values, mission, and the opportunity to contribute to innovative autonomous driving technologies. Share your passion for AI and how the role fits into your career aspirations, showing your enthusiasm for being part of the team.