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Senior Software Engineer, Applied ML and Evaluation

Waymo is an autonomous driving technology company with the mission to be the most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo One, a fully autonomous ride-hailing service, and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over one million rider-only trips, enabled by its experience autonomously driving tens of millions of miles on public roads and tens of billions in simulation across 13+ U.S. states.

The Perception Scene Semantics team is responsible for developing ML models for scene understanding around construction, emergency scenes, mapless driving and world changes. The team works on the end to end ML pipeline from data mining, labeling, training onboard / offboard models, and deployment. We need someone with strong ML experience to drive the architecture upgrades and product feature development.

In this hybrid role, you will report to a TLM within Perception Semantics.

You will:

  • Design and implement new evaluation systems and strategies to measure the safety and performance of large-scale Waymo Perception machine learning models and support onboard engineers in both regression prevention and quality hillclimbing.
  • Make evaluation-driven recommendations for modeling and data changes and actively influence deployment of onboard models.
  • Create evaluation strategies that leverage advanced ML strategies and models, like VLMs, active learning

You have:

  • 2+ years of experience in industrial AI applications involving the creation, maintenance, and evaluation of ML products
  • Ability to drive the development of new products
  • Experience in JAX (Preferred), TensorFlow (Preferred), or PyTorch
  • Functional knowledge of C++ programming
  • Passion for developing novel ways of measuring the performance of mission-critical ML systems
  • Experience evaluating the quality of machine learning prediction at scale

We prefer:

  • Background in both computer vision and general machine learning.
  • Experience using and developing distributed systems
  • Good knowledge of LLM/VLM

#LI-hybrid

The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. 

Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. 

Salary Range
$204,000$259,000 USD
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Average salary estimate

$231500 / YEARLY (est.)
min
max
$204000K
$259000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Senior Software Engineer, Applied ML and Evaluation, Waymo

Waymo is on the cutting edge of autonomous driving technology, and we’re looking for a passionate Senior Software Engineer specializing in Applied ML and Evaluation to join our vibrant team in Mountain View, California, or Bellevue, Washington. You will play a pivotal role in refining the Waymo Driver, the world's most experienced driver™, as we strive to offer a safer, more reliable mobility solution and significantly reduce traffic-related fatalities. In this role, you'll immerse yourself in the fantastic world of machine learning, working directly with our Perception Scene Semantics team. This team takes the wheel in developing innovative ML models aimed at enriching scene understanding, particularly in complex scenarios like construction zones and emergency response situations. Your expertise will help shape the end-to-end ML pipeline, from data mining and labeling to model training and deployment. You’ll design cutting-edge evaluation systems that gauge our models' safety and performance while collaborating with onboard engineers to elevate quality standards. If you’re excited about pushing the boundaries of ML and want to help define the future of autonomous driving, we’d love to see how you can enhance our efforts in creating robust evaluation strategies with advanced ML techniques. Join us, and let’s together pave the road to a safer tomorrow!

Frequently Asked Questions (FAQs) for Senior Software Engineer, Applied ML and Evaluation Role at Waymo
What are the main responsibilities of a Senior Software Engineer, Applied ML and Evaluation at Waymo?

As a Senior Software Engineer in Applied ML and Evaluation at Waymo, your primary responsibilities will include designing and implementing evaluation systems to measure the performance of our large-scale ML models. You’ll also be making evaluation-driven recommendations for data and modeling changes, actively shaping the deployment of our onboard models. Collaborating with the Perception Scene Semantics team, you will utilize advanced ML strategies to enhance safety and efficiency in our autonomous driving technologies.

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What qualifications are necessary for the Senior Software Engineer role at Waymo?

To thrive as a Senior Software Engineer in Applied ML and Evaluation at Waymo, candidates should have at least 2 years of experience in industrial AI applications and demonstrate strong ML product evaluation skills. Proficiency in ML frameworks like JAX, TensorFlow, or PyTorch is preferred, alongside functional knowledge of C++. A solid background in computer vision and general machine learning, particularly in evaluating the quality of predictions at scale, will be highly valuable.

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What technical skills are required for the Senior Software Engineer, Applied ML and Evaluation at Waymo?

The Senior Software Engineer role at Waymo requires a robust understanding of machine learning and AI technologies. Proficiency in ML frameworks such as JAX, TensorFlow, or PyTorch is essential, alongside a foundational knowledge of C++. Familiarity with concepts regarding large language models (LLMs) and vision-language models (VLMs) will also be advantageous, as will experience in developing and utilizing distributed systems for machine learning applications.

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How does the Senior Software Engineer position contribute to the mission at Waymo?

The Senior Software Engineer in Applied ML and Evaluation plays a crucial role in advancing Waymo’s mission to create the most trusted and safe autonomous vehicles. By developing robust ML evaluation frameworks, you will help ensure that our models operate effectively under real-world conditions, directly influencing safety and user experience. Your contributions in evaluating and improving machine learning models will help Waymo reduce traffic fatalities and improve mobility access for everyone.

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What is the expected salary range for the Senior Software Engineer, Applied ML and Evaluation position at Waymo?

The expected base salary range for the Senior Software Engineer, Applied ML and Evaluation position at Waymo ranges from $204,000 to $259,000 USD based on job-related factors. Actual starting pay may vary depending on experience, location, relevant training, and skill level. Additionally, Waymo offers participation in an annual bonus program, an equity incentive plan, and a comprehensive benefits program.

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Common Interview Questions for Senior Software Engineer, Applied ML and Evaluation
Can you explain your experience with machine learning frameworks like JAX, TensorFlow, or PyTorch?

When answering this question, highlight specific projects where you have implemented these frameworks. Discuss how you leveraged their unique features to tackle complex problems, optimizing model performance or improving evaluation systems.

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What strategies would you employ to improve evaluation metrics for ML models?

In your response, detail various evaluation strategies and metrics you’ve used in the past. Explain how you assess a model's performance, using examples from your experience to showcase your approach in enhancing model reliability and accuracy.

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Describe a challenging problem you faced in an ML project and how you overcame it.

Use the STAR method to describe your situation, task, action, and results. Make sure to emphasize your problem-solving skills, creative thinking, and the successful outcomes that followed your actions.

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How do you ensure the safety of autonomous ML systems?

Discuss the importance of rigorous testing, ongoing validations, and the implementation of comprehensive evaluation frameworks to ensure model safety. Provide examples illustrating how you maintain safety standards in your projects.

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How would you collaborate with a cross-functional team to develop ML products?

Emphasize your ability to communicate effectively with team members from diverse backgrounds, from engineering to product management. Share experiences where collaboration led to successful ML product development and the impact it had on your projects.

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What is your approach to handling data quality issues in your ML models?

Outline your methodology for identifying data quality issues and the steps you take to rectify them. Mention tools or processes you’ve employed to ensure high-quality input data for training and validation of your models.

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Can you discuss your understanding of distributed systems in the context of ML?

Explain your experience with distributed computing frameworks and how they facilitate scalability in ML applications. Share insights into performance optimization and fault tolerance, drawn from your hands-on work with distributed systems.

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What role does active learning play in your evaluation strategies?

Describe how active learning can enhance model performance by focusing on difficult predictions. Share your strategy for implementing active learning in ML projects and the benefits you've observed in your past work.

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How do you keep yourself updated with the latest developments in ML technology?

Demonstrate your commitment to continuous learning by mentioning industry resources, conferences, online courses, or communities you follow. Discuss how you've applied new findings or technologies in your recent work.

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What are the most important performance metrics you consider when evaluating ML models?

Discuss the key performance metrics relevant to your work, such as accuracy, recall, precision, and F1 score. Explain the context in which each metric is most applicable and how you use them to measure model success.

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Waymo’s mission is to make it safe and easy for people and things to move around. With the Waymo Driver, we can improve the world’s mobility while saving thousands of lives.

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Social Impact Driven
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Collaboration over Competition
Growth & Learning
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Full-time, hybrid
DATE POSTED
March 26, 2025

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