Machine Learning Engineer

As a Machine Learning Engineer at Wayve, your primary responsibilities include developing and productionizing end-to-end neural networks for autonomous driving. You'll work closely with both Production and Research teams to test, scale, and deploy advanced machine learning models. Your expertise will help shape the future of AI in automotive technology, ensuring our solutions address real-world challenges effectively.

To become a Machine Learning Engineer at Wayve, candidates must have at least 3 years of experience in shipping AI and ML features in production settings, strong Python programming skills, and solid understanding of deep learning methodologies. Familiarity with frameworks such as PyTorch, as well as experience in designing efficient machine learning systems, is crucial for this role.

At Wayve, we foster a vibrant and inclusive work environment that blends collaborative office time with the flexibility of remote work. Our hybrid working policy is designed to encourage innovation and team building, while respecting individual working styles and preferences. Your contributions will be valued as we collectively shape the future of transportation technology.

Yes! Wayve is committed to continuous learning and development. As a Machine Learning Engineer, you'll have access to various resources, training sessions, and workshops that will help you grow your skills and expertise. We believe in investing in our team's career growth to ensure they can make impactful contributions.

As a Machine Learning Engineer at Wayve, you'll engage in innovative projects related to designing and implementing cutting-edge neural networks tailored for autonomous driving. This includes developing data pipelines, optimizing models, and collaborating with cross-functional teams to push the boundaries of AI technology in real-world applications.

Highlight specific frameworks you've used, such as PyTorch, TensorFlow, or others. Discuss projects where you implemented these frameworks successfully, focusing on your role in model development and the outcomes of your work.

Discuss your methodology for improving model performance, addressing aspects such as hyperparameter tuning, feature selection, and the trade-offs of different optimization techniques. Provide examples from past experiences to illustrate your strategies.

Explain the elements of creating a robust multimodal data pipeline, including data ingestion, curation, processing, and integration. Emphasize your understanding of how these components work together to facilitate machine learning tasks.

Reflect on specific challenges you've encountered, such as scalability, data quality, or integration issues. Discuss the solutions you implemented, showcasing your problem-solving skills and technical expertise.

Choose a project that involved translating theoretical concepts into practical applications. Discuss your role, the steps taken to move it into production, and the impact it had on the organization or stakeholders.

Describe your approach to dataset management, emphasizing methods for validation, cleaning, and ensuring diversity within data. Mention any tools or techniques you've used to maintain high-quality datasets.

Detail your familiarity with various validation techniques such as k-fold cross-validation, A/B testing, or others. Share examples of how you've implemented these methodologies to assess model performance effectively.

Discuss your strategies for keeping current with industry trends, such as participating in conferences, reading research papers, or engaging in online courses. Highlight your commitment to lifelong learning in this dynamic field.

Share your passion for technology and its potential to transform transportation. Discuss how you envision contributing to the advancements in autonomous driving, enhancing safety, usability, and efficiency.

Express your admiration for Wayve's mission and values, emphasizing your enthusiasm for collaborating with a diverse team of innovators. Highlight how your skills align with the company's goals and your desire to make a meaningful impact on the future of mobility.