Staff Machine Learning Engineer Waymo London, England, United Kingdom

Staff Machine Learning Engineer Waymo London, England, United Kingdom

Full-Time No working from home possible
Neura Market

At a Glance

  • Tasks: Develop and optimise machine learning systems for autonomous driving technology.
  • Company: Waymo, a leader in autonomous driving innovation.
  • Benefits: Competitive salary, bonus program, equity incentives, and generous benefits.
  • Other info: Collaborative environment with opportunities to lead impactful projects.
  • Why this job: Join a mission-driven team transforming mobility and saving lives with cutting-edge technology.
  • Qualifications: M.S. or Ph.D. in relevant fields and 7+ years of ML experience.

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 ML Core London team builds and operates scalable machine learning systems, simulation workflows, and insight tools designed to improve the evaluation and developer onboarding journeys.
By combining expert human judgment with advanced machine learning models, we deliver training and evaluation data for hundreds of metrics and components that comprise the Waymo Driver.

We are looking for researchers and software engineers passionate about developing ML techniques for evaluation systems and driving performance improvements across our technology stack.

You will:

  • Build scalable systems for training and fine-tuning large-scale generative models to produce realistic and evaluate interesting driving behaviors.
  • Lead the implementation, and iteration of novel RL algorithms, reward functions, and training paradigms tailored for generating high-fidelity and insightful driving behaviors
  • Lead the development of cutting- edge Deep Learning models and Generative AI (LLM/VLM) solutions to enhance human-led triaging, introduce automation for high-volume workflows, and perform nuanced analysis of self-driving behavior to detect critical anomalies.
  • Oversee the production and optimization of machine learning models aiming to assess Waymo’s expansive fleet of vehicles that cumulatively travel millions of miles.
  • Proactively monitor and assimilate best practices from within Alphabet and the broader industry to develop a novel Reinforcement Learning from Human Preference (RLHF) based data collection and evaluation system.
  • Collaborate closely with multiple teams (e.g., Prediction, Planning, Research), other technical leads, and senior leaderships across Waymo to deliver on key strategic efforts.

You have:

  • M.S. or Ph.D. degree Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience.
  • 7+ years of hands-on experience in developing and applying Machine Learning models, with a significant focus on Reinforcement Learning.
  • Demonstrated expertise in deep learning, sequence modeling, and generative models.
  • Strong publication record or history of impactful project delivery in RL or related areas.
  • Proficiency in Python and standard ML frameworks (e.g., JAX, TensorFlow).
  • Experience with large-scale distributed training and data processing.
  • Proven ability to lead complex and ambiguous technical projects from conception to completion.

We prefer:

  • 10+ years of relevant experience in ML/RL research and application.
  • Experience in the autonomous vehicles domain, robotics, or complex simulation environments.
  • Deep understanding of state-of-the-art RL techniques, including those used for fine-tuning large models (e.g., from human feedback/preferences).
  • Familiarity with large-scale simulation platforms and their integration with ML training workflows.
  • Experience designing and using metrics for evaluating complex AI systems.
  • Track record of technical leadership, influencing senior stakeholders, and driving innovation across team boundaries.
  • Excellent communication skills, with the ability to articulate complex technical concepts clearly.

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

£150,000 — £162,000 GBP

#J-18808-Ljbffr

Staff Machine Learning Engineer Waymo London, England, United Kingdom employer: Neura Market

Cognition is an exceptional employer, offering a dynamic work environment where innovation thrives and employees are empowered to tackle some of the world's most significant challenges in AI. With a small, talent-dense team comprised of industry leaders from top tech companies, employees benefit from unparalleled mentorship and growth opportunities while contributing to groundbreaking projects like Devin and Windsurf. Located in Europe, our collaborative culture fosters creativity and ambition, making it an ideal place for those looking to make a meaningful impact in the field of applied AI.

Neura Market

Contact Details:

Neura Market Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Staff Machine Learning Engineer Waymo London, England, United Kingdom

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 Neura Market 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 Neura Market.

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 Neura Market.

Explore Job Boards Specifically for Tech Roles

Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Neura Market that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!

We think you need these skills to ace Staff Machine Learning Engineer Waymo London, England, United Kingdom

Machine Learning
Reinforcement Learning
Deep Learning
Generative Models
Python
JAX
TensorFlow

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 Neura Market.

Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Neura Market 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 Neura Market

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 Neura Market 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.