Senior Staff Machine Learning Engineer, Simulation job at Waymo in New York City, NY, San Francisco, CA, Mountain View, CA
Salário Estimado
R$ 12.870,00 - R$ 19.305,00
Tecnologias
Regular
Score da Vaga
Descrição da Vaga
Title: Senior Staff Machine Learning Engineer, Simulation Location: Mountain View, California, United States | San Francisco, California, United States | New York City, New York, United States Job Description: Waymo is an autonomous driving technology company with the mission to be the world's 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’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases.
The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.
S. states.
In this hybrid role, you will report to the Sr.
Staff Manager.
You will: Scale the development of machine learning-based metrics and eval datasets at Waymo with a mixture of strategic and hands-on contributions to solve our toughest evaluation problems Address novel evaluation problems by contributing core improvements to our ML models and training regimes.
Develop and execute a strategy to democratize ML-based development and deployment of metrics and datasets across Waymo by improvements to modeling, mining, training, analysis, and deployment tools.
Train large, offboard models to generate “ideal” references against which to measure on-vehicle driving.
Provide technical mentorship, guidance, and thought leadership to other engineers within the team and across collaborating groups.
Guide and align multiple teams—including Driver Understanding, Simulation, System Engineering, Research, and Onboard Software—on a cohesive evaluation strategy, ensuring cross-functional alignment on goals and priorities.
You have: PhD degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience. 7+ years of hands-on experience in developing and deploying machine learning applications Extensive experience with the practical challenges around building, evaluating, and launching models Demonstrated expertise in deep learning, sequence modeling, and generative models.
Proficiency in Python and standard ML frameworks (e.g., JAX, TensorFlow).
Proven ability to lead complex and ambiguous technical projects from conception to completion.
We prefer: 10+ years of relevant experience in ML research and application.
Experience scaling and democratizing ML adoption across organizations.
Experience in the autonomous vehicles domain, robotics, or complex simulation environments.
Understanding of state-of-the-art RL techniques, including those used for fine-tuning large models (e.g., from human feedback/preferences).
Experience designing and using metrics for evaluating complex AI systems.
Track record of technical leadership, influencing senior stakeholders, and driving innovation across team boundaries.
Excellent communication skills, with the ability to articulate complex technical concepts clearly.
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 $281,000—$356,000 USD
Requisitos
- PhD degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience
- 7+ years of hands-on experience in developing and deploying machine learning applications
- Extensive experience with the practical challenges around building, evaluating, and launching models
- Demonstrated expertise in deep learning, sequence modeling, and generative models
- Proficiency in Python and standard ML frameworks (e.g., JAX, TensorFlow)
- Proven ability to lead complex and ambiguous technical projects from conception to completion
- 10+ years of relevant experience in ML research and application
- Experience scaling and democratizing ML adoption across organizations
- Experience in the autonomous vehicles domain, robotics, or complex simulation environments
- Understanding of state-of-the-art RL techniques, including those used for fine-tuning large models (e.g., from human feedback/preferences)
- Experience designing and using metrics for evaluating complex AI systems
- Track record of technical leadership, influencing senior stakeholders, and driving innovation across team boundaries
- Excellent communication skills, with the ability to articulate complex technical concepts clearly
Responsabilidades
- Scale the development of machine learning-based metrics and eval datasets at Waymo with a mixture of strategic and hands-on contributions to solve our toughest evaluation problems
- Address novel evaluation problems by contributing core improvements to our ML models and training regimes
- Develop and execute a strategy to democratize ML-based development and deployment of metrics and datasets across Waymo by improvements to modeling, mining, training, analysis, and deployment tools
- Train large, offboard models to generate “ideal” references against which to measure on-vehicle driving
- Provide technical mentorship, guidance, and thought leadership to other engineers within the team and across collaborating groups
- Guide and align multiple teams—including Driver Understanding, Simulation, System Engineering, Research, and Onboard Software—on a cohesive evaluation strategy, ensuring cross-functional alignment on goals and priorities
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