Staff ML Engineer – AV Simulation & Foundation Models (Hybrid UK) in London

Staff ML Engineer – AV Simulation & Foundation Models (Hybrid UK) in London

London Full-Time 60000 - 80000 Β£ / year (est.) No working from home possible
Waymo

At a Glance

  • Tasks: Build and operate scalable ML systems for ultra-realistic AV simulations.
  • Company: Waymo, a leader in autonomous vehicle technology.
  • Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
  • Other info: Collaborative environment with a focus on innovation and research.
  • Why this job: Join a pioneering team and shape the future of autonomous driving.
  • Qualifications: 7+ years in applied deep learning and production ML experience.

The predicted salary is between 60000 - 80000 Β£ per year.

Waymo is hiring in London for a hybrid role within the DUE Machine Learning team to build and operate scalable ML systems, simulation workflows, and evaluation tools for the Waymo driver. You will contribute to research, model training, and productionization of large models for ultra-realistic AV simulations.

The role requires 7+ years in applied deep learning and production ML, plus experience bringing research to production.

Staff ML Engineer – AV Simulation & Foundation Models (Hybrid UK) in London employer: Waymo

Waymo is an exceptional employer, offering a dynamic work environment in Greater London where innovation meets operational excellence. With a strong focus on employee growth and collaboration, we provide ample opportunities for professional development while ensuring a supportive culture that values diversity and creativity. Join us to be part of a pioneering team dedicated to enhancing the rider experience in one of the world's most vibrant cities.

Waymo

Contact Details:

Waymo Recruitment Team

We think you need these skills to ace Staff ML Engineer – AV Simulation & Foundation Models (Hybrid UK) in London

Python
SQL
Problem-Solving Skills
Data Engineering
Attention to Detail
Communication Skills
ETL/ELT Processes