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Machine Learning Engineer, Simulation

Waymo

London

Hybrid

GBP 90,000 - 97,000

Full time

26 days ago

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Job summary

An innovative company is seeking research engineers to advance ultra-realistic autonomous vehicle simulations using cutting-edge foundation models. In this exciting hybrid role, you'll collaborate with teams across locations to design and implement experiments that push the boundaries of simulation technology. Your contributions will directly impact the development of metrics for realism and the integration of large models into various applications. If you're passionate about machine learning and eager to tackle complex challenges in a collaborative environment, this opportunity is perfect for you.

Benefits

Discretionary annual bonus program
Equity incentive plan
Generous Company benefits program

Qualifications

  • 2+ years of experience in applied Deep Learning.
  • Strong coding and design skills required.

Responsibilities

  • Develop metrics to measure realism in AV simulations.
  • Collaborate with teams to enhance simulation realism.

Skills

Deep Learning
Coding
Machine Learning Techniques
Experiment Design

Job description

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 Simulator team builds state-of-the-art simulations of realistic environments for the testing and training of the Waymo driver. We use machine learning to model the real world, including realistic agents (vehicles, pedestrians, cyclists, motorcyclists etc.), roads, traffic control systems, and weather. To increase the fidelity and steerability of the simulations, we employ large foundation models, trained on our massive datasets that allow us to quickly setup and rollout multiple scenarios to subject our driver to.

The team in London works with teams in Mountain View, California and Oxford, UK to build these foundation models out and to integrate them into several evaluation and training products. We are looking for research engineers to work on these exciting problems.

In this hybrid role, you will report to an Engineering Manager.

You will:
  • Be part of a world-class research engineering team to grow the state-of-the-art of ultra-realistic AV simulations using foundation models.
  • Collaborate with teams in London, Oxford, and Mountain View to use large models to improve simulation realism.
  • Design experiments that push the frontiers of AV simulations.
  • Develop metrics that measure the realism of simulated worlds.
  • Train and evaluate large models and integrate them into the simulator and its downstream applications.
  • Help hire outstanding research engineers from diverse backgrounds.
  • Be a part of a collaborative research engineering team that takes research ideas and productionizes them.
We prefer:
  • 2+ years of experience in applied Deep Learning.
  • 2+ years of coding and design skills.
  • Experience solving production problems using state-of-the-art ML techniques.
  • Experience with Machine Learning research.

The expected base salary range for this full-time position 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. 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: £90,000 — £97,000 GBP

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