Staff Machine Learning Engineer

Staff Machine Learning Engineer

Full-Time 80000 - 100000 £ / year (est.) No working from home possible
Wayve

At a Glance

  • Tasks: Lead innovative projects in machine learning for autonomous driving and enhance vehicle intelligence.
  • Company: Wayve, a pioneering company in Embodied AI technology since 2017.
  • Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
  • Other info: Collaborative environment with mentorship opportunities and a focus on diversity.
  • Why this job: Make a real impact on the future of automated driving with cutting-edge technology.
  • Qualifications: 8+ years in ML engineering, deep learning expertise, and strong programming skills.

The predicted salary is between 80000 - 100000 £ per year.

About Us

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact. Make Wayve the experience that defines your career!

The Role

As a Staff/Principal ML Engineer within the Autonomy team, you’ll lead critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalization, comfort, and collaboration. You’ll design and deliver ML-driven behaviors that scale from assisted to autonomous driving. Your work will span across model architecture, data pipelines, evaluation frameworks, and real-world deployment. You’ll collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable, and ready for production.

What You’ll Be Working On

  • Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization.
  • Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment.
  • Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness.
  • C curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development.
  • Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems.
  • Collaborate cross-functionally across various teams to ensure integration and iteration velocity.
  • Mentor senior engineers and shape the long-term technical direction across Autonomy.

About You

Essential

  • 8+ years in ML engineering, with a strong track record of shipping deep learning systems to production.
  • Expert in deep learning (esp. sequential models, control, planning, or perception).
  • Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices.
  • Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components.
  • Ability to lead technical initiatives across teams, drive alignment, and mentor engineers.

Desirable

  • Prior work in autonomous driving, imitation learning, or trajectory prediction.
  • Familiarity with personalization, human behavior modeling, or driver intent inference.
  • Experience integrating ML systems into production hardware or multi-agent simulation.

We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.

Staff Machine Learning Engineer employer: Wayve

At Wayve, we pride ourselves on being a pioneering employer in the field of Embodied AI technology, offering a dynamic and inclusive work culture that values innovation and collaboration. Our commitment to employee growth is evident through mentorship opportunities and cross-functional projects that empower you to lead initiatives in autonomous driving. Located in a vibrant tech hub, we provide a stimulating environment where your contributions directly impact the future of automated driving, making Wayve not just a job, but a defining career experience.

Wayve

Contact Details:

Wayve Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Staff Machine Learning Engineer

Tip Number 1

Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.

Tip Number 2

Show off your skills! Create a portfolio showcasing your projects, especially those related to machine learning and autonomous systems. This gives potential employers a taste of what you can do and sets you apart from the crowd.

Tip Number 3

Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice common ML interview questions and be ready to discuss your past projects in detail. Confidence is key!

Tip Number 4

Don’t forget to apply through our website! We love seeing applications directly from passionate candidates. Tailor your application to highlight how your experience aligns with our mission at Wayve, and let’s make an impact together!

We think you need these skills to ace Staff Machine Learning Engineer

Machine Learning Engineering
Deep Learning
Sequential Models
Control Systems
Planning
Perception
Python

Some tips for your application 🫡

Show Your Passion for AI:When writing your application, let your enthusiasm for AI and autonomous driving shine through. We want to see how your experiences align with our mission to create groundbreaking solutions in this field.

Tailor Your CV and Cover Letter:Make sure to customise your CV and cover letter for the Staff Machine Learning Engineer role. Highlight relevant projects and skills that demonstrate your expertise in deep learning and real-time systems—this will help us see why you're a great fit!

Be Clear and Concise:Keep your application clear and to the point. Use straightforward language to describe your achievements and technical skills. We appreciate well-structured applications that make it easy for us to understand your qualifications.

Apply Through Our Website:Don’t forget to submit your application through our website! This ensures that we receive all your details correctly and helps us keep track of your application. We can’t wait to hear from you!

How to prepare for a job interview at Wayve

Know Your Stuff

Make sure you brush up on your deep learning knowledge, especially around sequential models and control systems. Be ready to discuss your past projects in detail, particularly those that involved real-time systems or robotics.

Showcase Collaboration Skills

Since the role involves working with various teams, prepare examples of how you've successfully collaborated in the past. Highlight any experiences where you led technical initiatives or mentored other engineers.

Prepare for Technical Questions

Expect to dive deep into technical discussions during the interview. Practice explaining your thought process behind architecture choices and training methodologies, as well as how you would approach building evaluation pipelines.

Embrace the Company Culture

Wayve values diversity and inclusion, so be sure to express your appreciation for different perspectives. Share how you’ve contributed to an inclusive environment in your previous roles, and show your enthusiasm for their mission to enhance automated driving.