Research Engineer Intern - Perception, Multi-Modal Occupancy Map Generation and Forecasting

As a Research Engineer Intern focusing on perception and multi-modal occupancy map generation at Plus, your core responsibilities will involve developing innovative models for occupancy map generation and forecasting. You will design and implement a pipeline for generating data, training, evaluating, and optimizing deep learning models. Additionally, you'll contribute to the real-time deployment of these models, engage in simulation testing, and have opportunities to influence research publications.

To apply for the Research Engineer Intern position at Plus, candidates should be pursuing a Master's or PhD in fields such as Computer Science, Electrical Engineering, mathematics, or statistics. Applicants should demonstrate a solid understanding of deep learning principles and be familiar with perception, prediction, and planning models. Proficiency in Python and experience with deep learning frameworks like PyTorch or TensorFlow are also essential.

Apart from the required qualifications, having past experience in designing and training models applicable to autonomous driving data will make your application shine for the Research Engineer Intern role at Plus. Familiarity with existing autonomous driving benchmarks and a publication record in recognized venues will also be advantageous. Additionally, knowledge of robotics and motion planning algorithms can elevate your profile.

For the Research Engineer Intern position at Plus, proficiency in Python is crucial, as it’s widely used for developing deep learning models. Familiarity with deep learning frameworks such as PyTorch and TensorFlow is also important as these tools will help you design, train, and evaluate your models effectively.

At Plus, the hourly compensation for the Research Engineer Intern position ranges from $19 to $65. This pay is determined based on factors such as your year in school, degree, and overall experience, ensuring that you are rewarded fairly for your contributions to the team.

When answering this question, highlight specific projects or coursework where you designed or implemented deep learning models. Discuss the frameworks you used, such as PyTorch or TensorFlow, and any particular challenges you faced and overcame during model training.

In your answer, summarize your understanding of perception and prediction models, perhaps mentioning any academic projects or research. It can be helpful to include examples of how these models are applied in real-world scenarios, emphasizing your analytical skills.

Here, describe the techniques you use for optimization, such as tuning hyperparameters, using regularization methods, or experimenting with different architectures. Highlight any practical experiences where these strategies resulted in improved model performance.

In your response, provide a specific example of a challenge, detailing the problem, your approach to investigate it, and the outcome. Be sure to focus on the skills you used and the learning experience you gained.

Discuss the resources you utilize to keep your knowledge current, such as following key researchers, reading industry papers, participating in forums, or attending conferences. Mention any relevant publications or recent discoveries that have inspired you.

Share a story of a collaborative project—what your role was, how you communicated and worked through challenges, and the success that stemmed from this teamwork. Emphasize the importance of collaboration in research and engineering.

Detail any coursework, projects, or personal work you've done related to computer vision. Discuss the methods you've implemented, such as image processing, object detection, or segmentation, and how they've related to your interest in autonomous driving.

In your answer, outline your thought process for designing a model architecture, including your considerations for input data, the specific tasks the model needs to perform, and any architectural choices you would make based on prior knowledge or research.

Mention tools such as CARLA or AirSim, describing your experience with them. Explain how you've used these simulators to validate models or test algorithms, showcasing your practical experience in simulation environments.

Express your enthusiasm for contributing to cutting-edge research at Plus. Discuss specific areas where you believe your skills could be impactful, and your desire to learn and grow within the company through meaningful projects.