Senior SWE, Perception ML/VLM

As a Senior Software Engineer specializing in Perception ML/VLM at Waymo, you will be responsible for applying sensor fusion and machine learning techniques to real-world challenges. Your main duties will include developing and deploying machine learning models, particularly focusing on vision language models (VLMs), and creating data mining, labeling, training, and evaluation pipelines that support onboard development.

Candidates for the Senior Software Engineer in Perception ML/VLM position at Waymo should have a Bachelor's degree in Computer Science or a related field, alongside at least 5 years of experience in machine learning or autonomous vehicles. Proficiency in Python and hands-on experience with deep learning frameworks such as PyTorch or TensorFlow are essential.

While it is not strictly necessary to have published work for the Senior Software Engineer in Perception ML/VLM role at Waymo, we prefer candidates who have publications in top-tier conferences such as CVPR or NeurIPS. This demonstrates your expertise and contribution to the field of machine learning and robotics.

In addition to the required qualifications, an advantage for the Senior Software Engineer in Perception ML/VLM role at Waymo would be advanced degrees (MS or PhD) in Computer Science or Robotics, along with experience in C++. Having a strong presence on GitHub or writing tech blogs about LLMs/VLMs can also enhance your candidacy.

The expected base salary range for the Senior Software Engineer in Perception ML/VLM position at Waymo is $204,000 to $259,000 USD. Actual pay may vary based on factors such as work location, experience, and education. During the hiring process, your recruiter can provide more details on the specific salary range for your location.

When discussing your experience with machine learning models, elaborate on specific projects where you applied techniques like supervised learning, unsupervised learning, or reinforcement learning. Provide examples of challenges you faced and how you solved them, demonstrating your analytical skills and technical competencies.

In your answer, describe your methodology, starting from data acquisition and preprocessing to model selection and evaluation. Discuss any frameworks you've used, such as TensorFlow or PyTorch, and provide insights into how you validate model performance and ensure robustness.

Share your experiences regarding deployment, emphasizing any challenges you overcame. Discuss the frameworks or tools you have used for deployment, such as Docker or Kubernetes, and outline how you ensure that models perform as expected once in the production environment.

Articulate your strategies for managing large datasets, including techniques for data cleaning, preprocessing, and feature engineering. Highlight your experience with data pipelines and any tools you’ve used for efficient data handling.

Discuss metrics relevant to the type of models you work with, such as accuracy, precision, recall, F1-score, or ROC-AUC. Explain your approach to model evaluation and how you balance these metrics based on the tasks at hand.

Choose a specific event or project where you identified a complex problem and detail the steps you took to analyze and resolve it. Explain your thought process and the impact of your solution on the overall project or organization.

In your response, mention the journals, conferences, or online courses you follow, and how you apply new knowledge to your projects. Highlight any communities or networks of fellow professionals you engage with to exchange ideas.

Address your communication skills and experience working with diverse teams. Provide an example of a project where collaboration was crucial, explaining how you coordinated efforts with product managers, researchers, and engineers to achieve shared goals.

Reflect on current industry challenges, such as data privacy, ethical considerations, or technical limitations. Offer insights into potential solutions or advancements that might mitigate these challenges, indicating your forward-thinking approach.

Personalize your response by discussing Waymo's mission, culture, and innovations. Explain what excites you about independent transportation solutions and how your skills align with their goals, positioning yourself as a candidate who is passionate about contributing to their success.