Research Engineer

As a Research Engineer at Applied Intuition, your primary responsibilities revolve around leading cutting-edge research initiatives in autonomous systems. You'll collaborate with the ADAS stack engineering and simulation teams to develop and deploy advanced algorithms for mass production vehicles. Conducting thorough research on architecture design, robustness, and behavior generation will also be crucial to your role. Furthermore, you'll work alongside talented Research Scientists to contribute to high-quality publications for top-tier conferences, ensuring that Applied Intuition remains at the forefront of the industry.

To be a successful candidate for the Research Engineer role at Applied Intuition, you should possess experience with end-to-end autonomy, multi-view perception, closed-loop behavior generation, and strong research skills. An MSc or PhD in machine learning, computer vision, or a related field is preferred, although hands-on experience in relevant applications can also be advantageous. A passion for scalable, data-driven autonomy and a collaborative spirit will make you a great fit for our dynamic team.

The Research Engineer at Applied Intuition plays a vital role in advancing our mission of accelerating the adoption of safe autonomous systems. By leveraging large-scale driving data and developing industry-leading models, you'll directly impact the performance of our autonomy stacks, allowing our customers to bring high-quality systems to market quicker. Your innovative research can lead to real-time enhancements in autonomous vehicles, influencing the automotive industry's future.

As a Research Engineer at Applied Intuition, you'll utilize a wide array of tools and technologies tailored to autonomous systems and machine learning. This includes advanced algorithms for simulation, data-driven generation techniques, and cutting-edge modeling frameworks. You'll also work with a diverse set of sensors and large-scale datasets to train and validate your models, ensuring they meet industry standards and customer needs.

The work environment at Applied Intuition is dynamic, collaborative, and customer-focused. As an in-office team, you’ll engage daily with talented colleagues who are equally passionate about creating revolutionary autonomous solutions. Our culture emphasizes learning, sharing knowledge, and continuous improvement, allowing you to thrive professionally while making impactful contributions to exciting projects.

In answering this question, share specific projects where you've applied multi-view perception techniques, highlighting your direct involvement in designing, implementing, or testing models. Discuss the outcomes and how these techniques improved the performance of autonomous systems, demonstrating your technical expertise and problem-solving abilities in real-world applications.

When addressing this question, explain that closed-loop behavior generation is crucial for creating responsive and adaptive autonomous systems. It allows vehicles to simulate real-time decision-making processes based on environmental feedback. Use examples from prior projects to illustrate your understanding of its practical implications and how you've applied it in your work.

Discuss your strategy for selecting research topics that align with industry trends and the expertise of your team. Talk about the importance of collaboration with peers on research papers and your familiarity with the submission processes for top-tier conferences. Highlight your experience in adequately preparing manuscripts and addressing feedback from peer reviews.

Share your experience with popular machine learning frameworks such as TensorFlow, PyTorch, or Keras, emphasizing how you’ve utilized them in research projects. Discuss specific algorithms you’ve implemented and how these tools have helped you in the development of scalable models for autonomous systems.

Use the STAR (Situation, Task, Action, Result) method to narrate a specific project that posed significant challenges. Focus on your role in identifying the problem, the solutions you brainstormed, and how you implemented those solutions. Conclude with the positive outcomes and lessons learned, demonstrating your problem-solving skills and resilience under pressure.

In your response, emphasize the methods you employ for testing and validating algorithms before deployment, such as simulating edge cases and using extensive validation datasets. Discuss your commitment to following best practices in safety and efficacy, and how you incorporate feedback to refine algorithms post-deployment.

Articulate how data is central to your research and training of models. Discuss your strategy for handling and interpreting large datasets, along with your approach to ensuring the quality and integrity of the data used in developing autonomy algorithms. Provide examples if applicable.

Describe your ability to work autonomously while also being an active collaborator. Talk about your experience in contributing to team projects, sharing knowledge with peers, and how you seek feedback on your work to enhance outcomes. Highlight the importance of both skills in advancing research initiatives.

Share your passion for the field by discussing your methods for staying updated with the latest research, trends, and powerful innovations. Mention journals, conferences, online courses, or community engagement that informs your work, showcasing your proactive approach to personal and professional development.

Convey your enthusiasm for Applied Intuition’s mission and how your interests align with their goals. Discuss specific aspects of the company culture, projects, or technologies that resonate with you. This reflection will show your genuine interest in contributing to their success while advancing your career.