2025 Summer Intern, PhD, Research - Preference Learning

As a 2025 Summer Intern, PhD, Research - Preference Learning at Waymo, you'll be responsible for implementing post-training algorithms, curating training datasets, running training experiments on planning models, and evaluating the performance of these models. This role is designed to provide you with hands-on experience in applied research within the field of autonomous driving.

Candidates for the 2025 Summer Intern, PhD, Research - Preference Learning position at Waymo should be currently enrolled in a PhD program in computer science, applied mathematics, or a closely related field. Basic programming skills in Python or C/C++ along with experience in machine learning and familiarity with modern deep learning frameworks (like PyTorch, JAX, or TensorFlow) are important qualifications for this role.

During your internship as a 2025 Summer Intern, PhD, Research - Preference Learning at Waymo, you'll have the opportunity to enhance your skills in machine learning, particularly in areas such as reinforcement learning, preference learning, and deep learning. You'll also gain valuable experience in curating datasets and running training experiments, giving you a solid foundation in practical applications relevant for the industry.

The 2025 Summer Intern, PhD, Research - Preference Learning position at Waymo is a hybrid onsite internship. This means you will have the flexibility to work both remotely and onsite, allowing for a balanced and dynamic work environment.

Waymo's internship program is structured to provide meaningful learning experiences, focusing on collaboration and mentoring. As a 2025 Summer Intern, PhD, Research - Preference Learning, you will work closely with industry experts and engage in projects that have real-world impacts, making your internship both rewarding and beneficial for your future career.

Preference learning is a subfield of machine learning focused on building models that predict preferences or rankings rather than specific outcomes. In the context of autonomous driving, preference learning can help improve decision-making processes, such as selecting the safest driving actions based on past driving scenarios. Be prepared to explain how this impacts the development of safe driving algorithms.

Discuss any projects or coursework where you've utilized frameworks like PyTorch, TensorFlow, or JAX. Highlight specific implementations you've worked on, the challenges you faced, and how you overcame them to develop models. Providing concrete examples will showcase your hands-on experience.

Explain your methodology for curating training datasets, including sourcing data, ensuring quality, and managing biases. You might mention techniques you've used to clean and process data, as well as how you validate that the datasets are effective for machine learning models.

Challenges include dealing with vast amounts of data, ensuring data privacy, creating models that generalize well across different scenarios, and the need for real-time processing. Explore your thoughts on mitigating these challenges and how they apply to Waymo's objectives.

Talk about a specific project where you applied reinforcement learning principles. Clearly articulate the problem you were solving, your approach, the results you achieved, and what you learned from the experience. It's important to show your understanding and practical involvement in the field.

Be ready to discuss any papers you've published or contributed to at relevant conferences. Detail the focus of your research, your specific contributions, and how these developments are relevant to the role you're applying for at Waymo.

Discuss the metrics and validation techniques you use to assess model performance, such as accuracy, precision, recall, and F1 score. Explain how you optimize model performance and handle cases of overfitting or underfitting.

Highlight specific resources like journals, websites, or conferences that you follow to keep up with the latest industry developments. Sharing your continuous learning habits demonstrates your commitment to growth in your field.

Articulate your reasons for choosing Waymo, focusing on its mission, cutting-edge research, and opportunities for growth. Convey your enthusiasm for working on projects that have a significant impact on society, reinforcing how your goals align with Waymo's objectives.

Discuss your collaborative experiences in research settings and how you contribute to a team's success. Provide examples where communication, shared goals, and problem-solving lead to successful outcomes. This showcases your interpersonal skills and ability to work in a team.