Machine Learning Engineer, ADAS in London

Machine Learning Engineer, ADAS in London

London Full-Time 60000 - 80000 £ / year (est.) Home office (partial)
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At a Glance

  • Tasks: Train and improve AI models for autonomous driving, tackling real-world challenges.
  • Company: Wayve, a pioneering developer of Embodied AI technology for automated driving.
  • Benefits: Competitive salary, equity options, hybrid working, and comprehensive health benefits.
  • Other info: Inclusive culture valuing diverse perspectives and offering excellent career growth.
  • Why this job: Join a mission-driven team shaping the future of mobility with cutting-edge AI.
  • Qualifications: Experience in computer vision and 3D perception; passion for autonomy is key.

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

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.

Wayve’s ADAS engineering teams build the perception and intelligence that power driver assistance in real-world driving. We work end-to-end: from creating high-quality training data, to developing and evaluating CV/3D perception models, to iterating quickly based on performance gaps. The team mixes “online” (on-car, latency/compute constrained) and “offline” (heavier, large-scale data generation) work, with a strong focus on measurable impact and shipping.

You’ll train, debug, and improve computer vision and 3D perception models, and iterate based on clear evaluation signals. You’ll work across the full ML lifecycle (data → training → evaluation → iteration), partnering with the team to decide what to tackle next based on where the system is underperforming. A meaningful portion of the role involves building scalable data pipelines (including auto-labelling / pseudo-labelling) to accelerate model development.

You’ll help deliver core ADAS perception capabilities such as detection, classification, and instance segmentation, with domain focus across lanes, objects, traffic signs, and traffic lights. You’ll contribute to offline pipelines like tracking + 3D reconstruction that let us back-propagate “known good” labels through time and generate large labelled datasets. Depending on your strengths, you may lean more into online models that must run fast in-car, or offline models that improve data quality and coverage at scale.

You should apply if you’ve built and shipped CV-focused deep learning systems and can demonstrate strong applied ML engineering (not research-only). You have experience with 3D perception concepts or pipelines (e.g., LiDAR, multi-view geometry, tracking, 3D reconstruction) and you’re comfortable owning work end-to-end, including evaluation and dataset generation. You enjoy pragmatic problem-solving, working under real product constraints, and you’re excited to improve real-world driving performance through better perception.

Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency.

Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere.

Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives.

The interview process is clear and respectful of your time: Initial call / recruiter screen (30 mins), Competency Interviews (Programming and System Design 2 hours total), Deep-dive technical interviews (domain-specific interview: 1 hour total), Final interview: mission & values alignment (1 hour). We’ll always explain the format and work around your availability.

What’s in it for you (Location dependant): Salaries benchmarked against the market annually, Meaningful equity, sharing in the ownership and long term success of Wayve, Relocation support and visa sponsorship where applicable, Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs, Learning and development budgets with support for training, conferences and growth, Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more.

Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know. 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.

At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.

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Contact Details:

Icehouseventures Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Machine Learning Engineer, ADAS in London

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We think you need these skills to ace Machine Learning Engineer, ADAS in London

Computer Vision
3D Perception
Deep Learning
Data Pipeline Development
Model Evaluation
Training Data Generation
LiDAR

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 Icehouseventures.

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

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How to prepare for a job interview at Icehouseventures

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 Icehouseventures 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

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