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2027 Summer Intern, Machine Learning, MS/PhD, Simulation Realism (World Model)

Waymo

Mountain View, CA · HybridFull-time$70/hrPosted todayStill listed today

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At a glance

Compensation
$70/hr
Location
Mountain View, CAHybrid
Role Type
Internship
Schedule
Full-time
Work Authorization
Not specified

Olive lists jobs from US employers, including remote roles you can work from the United States.

Job overview

Waymo is seeking a summer machine learning intern to develop and evaluate generative models for video and physical-world simulation. The intern will explore 3D/4D representations and methods for improving temporal consistency, realism, and inference speed, while collaborating with Waymo mentors and Google AI researchers to adapt world models for the simulation pipeline. Applicants must be pursuing an MS or PhD in a relevant field.

Skills & qualifications

RequiredNice to have

Skills

Deep LearningComputer VisionGenerative ModelingPythonPyTorchJAXVideo GenerationWorld Models3D/4D Scene Representations3D Computer VisionMulti-View GeometryPhysical/Geometric PriorsMultimodal Sensor DataOpen-Source ContributionsML PublicationsVision PublicationsRobotics Publications

Qualifications

MS or PhD in Related Field

Full 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.

Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you’re a software engineer or researcher who’s curious and passionate about Level 4 autonomous driving, we'd like to meet you.

Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship!

You will:

  • Implement, train, and evaluate generative models (such as diffusion models, flow matching, or autoregressive transformers) for video and physical world simulation.

  • Experiment with unified 3D/4D and spatiotemporal representations that generalize across diverse scenes, vehicle platforms, and sensor modalities.

  • Investigate conditioning, fine-tuning, or distillation methods to improve temporal consistency, physical realism, and inference speed for closed-loop simulation.

  • Partner with mentors and researchers across Waymo and frontier AI labs at Google to adapt foundation world models and translate research prototypes into Waymo’s simulation pipeline.

You have:

  • Currently pursuing an MS or PhD in Computer Science, Machine Learning, Computer Vision, Robotics, or a related field.

  • Solid background in deep learning and computer vision, with hands-on experience in modern generative modeling (e.g., diffusion models, flow matching, VAEs, or transformers).

  • Strong coding skills in Python and practical experience training and debugging models in PyTorch or JAX.

We prefer:

  • Prior research or projects in video generation, world models, or generalized 3D/4D scene representations.

  • Exposure to 3D computer vision, multi-view geometry, physical/geometric priors, or multimodal sensor data (Camera, LiDAR, Radar).

  • Contributions to open-source projects or publications in ML, vision, or robotics venues (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML).

Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in. The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements.

Hourly Masters Pay $70—$70 USD

The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements.

Hourly PhD Pay $85—$85 USD

How this job compares

Set against the 556 open Research Intern jobs on Olive Jobs, counted November 7, 2026.

Pay
  • This posting$70/hr
  • California median$38–85/hr74 hourly ranges
  • US median$25–44/hr236 hourly ranges

This rate, $70/hr, is 6% above the California median midpoint ($66/hr) — in the top quarter of Research Intern hourly ranges in California.

Openings
Olive Jobs lists 152 open Research Intern jobs in California and 556 across the US. 1% of them nationwide are listed as remote or flexible.
Hiring the most in California
Experience
Research Intern postings that state a minimum ask for 2 years of experience at the median; the middle half ask for 1 to 2 years (21 postings).
Education
243 of the 556 open Research Intern jobs (44%) ask for a bachelor's degree or higher.

Counted across the open, public US postings on Olive Jobs on November 7, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.

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