Research Scientist, World Model Post-Training

Waymo

Mountain View +2 more

Hybrid

Who this role is best for

Aimed at senior AI researchers with generative modeling expertise who can collaborate across teams and work in hybrid arrangements.

Best fit for

Senior AI researchers with generative modeling and reinforcement learning expertise in autonomous vehicles
— “Experience with autonomous vehicles”
Candidates with a PhD in deep learning and 3+ years of training large foundational models
— “Masters or PhD in deep learning in generative / world modeling”
Individuals who can design and execute complex experiments with diffusion or flow matching models
— “Design compelling experiments by training and evaluating large generative models”

Things to consider

The role requires a strong focus on post-training of foundational models, which may demand specialized knowledge
— “Experience with post-training of foundational models”
The hybrid work arrangement may require periodic in-person attendance in one of the listed US locations
— “report to a Software Engineering Manager”

How to stand out

Highlight experience with post-training techniques and reinforcement learning in your resume and interviews
— “post-train Waymo’s world model”
Showcase your ability to automate workflows and write high-quality documentation using AI tools
— “Leverage AI to write high quality code, unit tests, and documentation, and automate workflows”
Demonstrate familiarity with diffusion or flow matching models through specific project examples
— “Experience with diffusion / flow matching models”

Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Industry

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

world_model_research
post_training_of_world_models
large_generative_model_training
presentation_of_results
automation_of_workflows

Typical background

research_scientistmachine_learning_engineerdata_scientist

Skills & requirements

Required

World_model_research_and_developmentPost_training_of_world_modelsLarge_generative_model_training_and_evaluationPresentation_of_resultsCode_quality_automationUnit_tests_and_documentation

Preferred

Post_training_of_foundational_modelsReinforcement_learning_for_diffusion_modelsDistillationAutonomous_vehicle_experience

Stack & domain

JAXTensorflow 2PyTorchLeadershipCommunicationMachine LearningAIAutonomous Driving

About the role

Original posting from Waymo

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.
The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.
In this hybrid role, you will report to a Software Engineering Manager.
You will:
Conduct applied world model research and development
Post-train Waymo’s world model
Design compelling experiments by training and evaluating large generative models
Present results to peers and leadership
Leverage AI to write high quality code, unit tests, and documentation, and automate workflows
You have:
Masters or PhD in deep learning in generative / world modeling
3+ years of experience with modern deep learning frameworks (e.g., JAX, TensorFlow 2, Pytorch)
Experience with diffusion / flow matching models
Experience with reinforcement learning
Experience training large foundational models
We prefer:
Experience with post-training of foundational models
Experience with reinforcement learning for diffusion / flow matching models
Experience with distillation
Experience with autonomous vehicles
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$213,000—$263,000 USD

Source: Waymo careers