2025 Summer Intern, MS/PhD, Waymo Planner Technology - ML/DL Engineer

As the 2025 Summer Intern, MS/PhD, in Planner Technology at Waymo, your main responsibilities will include collaborating with a team of researchers and engineers to design and implement deep reinforcement learning models for autonomous vehicle planning. You'll analyze model performance, troubleshoot failure cases, and identify improvement opportunities. Staying updated on the latest advancements in autonomous driving and machine learning is also critical in this role.

To qualify for the 2025 Summer Intern, MS/PhD, Waymo Planner Technology position, you must be currently pursuing a Master's or PhD in a relevant field such as Computer Science, Machine Learning, Robotics, or a related area. Experience with deep learning concepts, proficiency in Python, and familiarity with frameworks like PyTorch or TensorFlow are essential. Preferable qualifications also include experience in the autonomous driving domain and proficiency in C++.

As a Waymo intern, particularly in the Planner Technology program, you’ll work on impactful projects that involve designing deep reinforcement learning models for enhancing autonomous vehicle planning. You’ll collaborate closely with experts and have the chance to contribute directly to innovations that aim to make roads safer and improve mobility through cutting-edge technology.

The expected hourly pay for the 2025 Summer Intern, MS/PhD, Waymo Planner Technology position varies based on your educational background. For Master's students, the pay is $50.48 per hour, while PhD candidates can expect to earn $60.10 per hour. Additionally, interns may participate in the company’s generous benefits programs, subject to eligibility requirements.

The 2025 Summer Intern, MS/PhD, Waymo Planner Technology position is based on-site at Waymo's headquarters in Mountain View, CA. This will be a hybrid internship, allowing for a combination of in-person collaboration and flexible work arrangements.

When approaching the design of deep reinforcement learning models for autonomous vehicles, it's important to first understand the required capabilities of the vehicle's planning system. I would focus on the model architecture, the types of data input it will receive, and how the model will learn from its environment. Continuous experimentation and performance analysis, along with collaboration with domain experts, can lead to iterative improvements in the model.

In my experience with machine learning frameworks like PyTorch and TensorFlow, I have utilized them for building, training, and evaluating models. I prefer PyTorch for its dynamic computation graph and ease of use, particularly when prototyping. TensorFlow has been beneficial for larger-scale deployments, where advanced features can optimize model performance and training efficiency.

Integrating ML models into existing systems often comes with challenges such as ensuring data compatibility, managing latency, and optimizing performance. It's crucial to work with hardware specifications and system architecture constraints from the outset. Proactively addressing issues related to model deployment, such as real-time data processing and system resource usage, can help mitigate these challenges.

I keep up with advancements in autonomous driving and machine learning by regularly reading research papers, following industry news, and participating in relevant forums or conferences. Engaging with peers, attending workshops, and taking online courses also allow me to enhance my knowledge and stay current with cutting-edge developments.

My experience includes establishing key performance metrics, running evaluations against test datasets, and employing various techniques such as hyperparameter tuning, data augmentation, and algorithm adjustments to improve model performance. Continuously monitoring performance post-deployment is also necessary to identify areas for further optimization.

In a recent project, I developed a convolutional neural network for image classification. My responsibilities included data preprocessing, model selection, and hyperparameter optimization. The challenge was to improve accuracy while minimizing overfitting, which I addressed through techniques like dropout regularization and cross-validation.

I would explain deep reinforcement learning as a way for algorithms to learn from their environment via trial and error. Just like training a dog, the model receives rewards for positive actions and penalties for negative ones, gradually learning to make better decisions. Combining reinforcement learning with deep learning allows systems to handle complex problems by recognizing patterns in vast amounts of data.

I am proficient in Python for data manipulation and machine learning, utilizing libraries like NumPy and pandas. Additionally, I have experience with C++, which is beneficial for performance-critical components in autonomous systems. Familiarity with other tools such as Jupyter Notebooks for experiments and version control systems like Git enhances my collaborative work.

Collaboration in a team setting is essential, especially in technical projects. I prioritize clear communication, ensuring everyone is aligned on tasks and goals. Utilizing collaborative tools like Trello or Slack helps track progress and fosters regular updates. By embracing feedback and being open to different perspectives, I strive to create a positive and productive team environment.

The most exciting aspect of working on autonomous driving technology is the potential to transform transportation and make roads safer for everyone. Contributing to innovations that could redefine mobility, reduce traffic incidents, and enable accessible transportation for all is incredibly motivating. It's rewarding to be part of a forward-thinking team pushing boundaries in this groundbreaking field.