Staff Machine Learning Engineer (TLM) (Driver Understanding and Evaluation)

Staff Machine Learning Engineer (TLM) (Driver Understanding and Evaluation)

Full-Time 60000 - 80000 £ / year (est.) Home office (partial)
Waymo

At a Glance

  • Tasks: Join a team to develop cutting-edge machine learning for autonomous vehicles.
  • Company: Waymo, a leader in autonomous driving technology with a mission to save lives.
  • Benefits: Competitive salary, bonus opportunities, equity plans, and generous benefits.
  • Other info: Collaborative environment with opportunities for career growth and innovation.
  • Why this job: Make a real impact on the future of mobility and improve road safety.
  • Qualifications: 7+ years in Deep Learning, coding skills, and experience in production environments.

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

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 DUE Machine Learning team will build and operate scalable machine learning and data systems, simulation workflow and insight tools, improve and speed up the evaluation and onboard developer journeys. It will combine expert human judgements and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo driver. We are planning to set up a team in London UK to work with the teams in MTV and Oxford to build these foundation models out and to integrate them into several evaluation and training products.

We are looking for researchers and software engineers who are passionate about developing machine learning techniques for the Evaluation systems on our autonomous vehicles, and have an incessant drive to improve the performance of our technology stack. In this hybrid role, you will report to an Engineering Manager.

  • 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 Waymo Oxford to use large models to improve sim 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

1+ years of people management experience.

We prefer:

  • 7+ years experience in applied Deep Learning
  • 7+ years coding and design skills
  • Experience solving complex production problems using state‑of‑the‑art ML techniques
  • Experience taking research to production
  • Expertise in Data Analysis or Data Science

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: £155,000—£163,000 GBP.

Staff Machine Learning Engineer (TLM) (Driver Understanding and Evaluation) 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

StudySmarter Expert Advice🤫

We think this is how you could land Staff Machine Learning Engineer (TLM) (Driver Understanding and Evaluation)

Join Local Tech Meetups

Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Waymo or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!

Contribute to Open Source Projects

Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Waymo.

Tap into Online Developer Communities

Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Waymo.

Explore Job Boards Specifically for Tech Roles

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We think you need these skills to ace Staff Machine Learning Engineer (TLM) (Driver Understanding and Evaluation)

Machine Learning
Deep Learning
Data Analysis
Simulation Workflow
Experiment Design
Metric Development
Software Engineering

Some tips for your application 🫡

Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.

Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Waymo.

Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Waymo and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!

Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!

How to prepare for a job interview at Waymo

Brush Up on Your Coding Skills

For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.

Know Your Tools and Frameworks

Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Waymo uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.

Showcase Your Projects

Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.

Prepare for Behavioural Questions

While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.