Software Engineer Intern- Controls

As a Software Engineer Intern - Controls, your primary responsibilities include designing, implementing, and enhancing control algorithms, integrating MPC with learning approaches, and migrating the Control QP solver while evaluating its performance. You'll also be involved in developing online adaptation techniques for managing vehicle behavior drift, analyzing trade-offs in learning strategies, and designing regression test suites to evaluate simulation capabilities.

To qualify for the Software Engineer Intern - Controls position, candidates must be pursuing an MS or PhD in Robotics, Computer Science, or a related field. Hands-on application skills in areas such as adaptive and nonlinear control, robust control, and Kalman filters are essential. A strong foundation in motion control and proficiency in programming languages like Python and C++ are also required.

In addition to the required qualifications, experience with Imitation Learning, Reinforcement Learning, and modern Neural Network architectures will significantly enhance your application as a Software Engineer Intern - Controls. Familiarity with model development and training using frameworks like PyTorch is also preferred, making you a more competitive candidate.

As a Software Engineer Intern - Controls, you will work on critical projects aimed at improving the autonomy stack, such as developing control algorithms, managing vehicle behavior drift, and implementing effective simulation capabilities. The hands-on experience you gain will be invaluable as you collaborate with experts and tackle real-world challenges in robotics.

The pay range for the Software Engineer Intern - Controls position varies between $19 to $65 an hour, depending on your experience and qualifications. This competitive compensation reflects our commitment to attracting top talent in the robotics field.

In your response, discuss your understanding of both MPC and learning-based methods. Explain the advantages of using MPC for predictive control while enhancing it with data-driven techniques, providing examples or theoretical concepts that relate to their integration.

Highlight the steps you would take to migrate the Control QP solver, including assessing existing architectures, benchmarking its performance through real-world scenarios, and ensuring robustness in different operational conditions to validate your migration process.

Discuss various online adaptation techniques you might implement, such as adaptive control strategies or real-time adjustments based on feedback. Provide examples of how these techniques can effectively manage and correct behavior drift in autonomous vehicles.

Share your hands-on experiences in debugging, including specific tools and methodologies that you've employed within Linux environments. Discuss examples where you've successfully resolved issues and how that experience positions you well for the Software Engineer Intern - Controls role.

Explain Kalman filters as tools for estimation and their relevance in motion control. Discuss any direct experience you have implementing Kalman filters in projects, including how they contributed to improving system performance and accuracy.

Discuss the pros and cons of both online learning and offline updates, emphasizing the contexts in which each approach excels. Share experiences or examples where you've navigated these trade-offs in your academic or project work.

Demonstrate your understanding of modern neural network architectures by discussing their applications in robotics. Highlight any projects where you've successfully applied these architectures and the impact they had on your results.

Detail the key components of a regression test suite, including test scenarios and performance metrics. Discuss the importance of maintaining a suite to ensure robust simulation capabilities in autonomous systems.

Reflect on potential challenges such as complex problem-solving or collaboration hurdles, and outline your strategies for addressing them effectively. Emphasize your problem-solving skills and adaptability.

Share a specific example of a collaborative project where you contributed significantly. Highlight your communication skills and how you navigated teamwork challenges, showcasing your ability to work effectively with peers.