At a Glance
- Tasks: Train and improve AI models for advanced vehicle perception and navigation.
- Company: Wayve, a leader in Embodied AI technology.
- Benefits: Competitive salary, equity, hybrid work, and learning budgets.
- Other info: Diverse and inclusive culture with excellent career growth opportunities.
- Why this job: Join a cutting-edge team shaping the future of driving technology.
- Qualifications: Experience in computer vision and applied machine learning engineering.
The predicted salary is between 60000 - 80000 £ per year.
Location: London, United Kingdom
Overview
Wayve is a leading developer of Embodied AI technology, creating advanced AI software and foundation models that enable vehicles to perceive, understand, and navigate complex environments.
Responsibilities
- As a Machine Learning Engineer on the ADAS engineering team, you will train, debug, and improve computer vision and 3D perception models, iterate across the ML lifecycle, and build scalable data pipelines.
What You'll Be Working On
- Deliver core ADAS perception capabilities such as detection, classification, and instance segmentation for lanes, objects, traffic signs, and traffic lights.
- Develop offline pipelines like tracking and 3D reconstruction to generate large labeled datasets.
- Decide whether to focus on online models that must run fast in-car or offline models that enhance data quality and coverage at scale.
Qualifications
- Experience building and shipping CV-focused deep learning systems.
- Strong applied ML engineering skills (not research-only).
- Experience with 3D perception concepts or pipelines (e.g., LiDAR, multi‑view geometry, tracking, 3D reconstruction).
- Ability to own work end‑to‑end: evaluation, dataset generation, and iteration under product constraints.
- Pragmatic problem‑solving mindset and passion for improving real‑world driving performance.
Interview Process
- Initial call / recruiter screen (30 minutes)
- Competency interviews: programming and system design (2 hours total)
- Deep‑dive technical interviews (domain‑specific, 1 hour total)
- Final interview: mission & values alignment (1 hour)
Benefits
- Salaries benchmarked against the market annually
- Meaningful equity
- Relocation support and visa sponsorship where applicable
- Hybrid working model and flexible core hours
- Learning and development budgets, training, conferences
- Comprehensive health benefits, dental, maternity and paternity leave, retirement or pension options, wellbeing partnerships, team socials
EEO Statement
Wayve is 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 or any other basis as protected by applicable law.
Machine Learning Engineer, ADAS in London employer: Necessary Ventures
Wayve is an exceptional employer, offering a dynamic work environment in the heart of London where innovation meets inclusivity. As a Data Scientist, you'll not only contribute to cutting-edge AI technology but also benefit from a hybrid working policy that promotes work-life balance and fosters professional growth through collaborative projects and diverse perspectives. Join us to be part of a forward-thinking team dedicated to optimising fleet operations while enjoying a culture that values every individual's unique contributions.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer, ADAS in London
✨Join Local Tech Meetups
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We think you need these skills to ace Machine Learning Engineer, ADAS in London
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 Necessary Ventures.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Necessary Ventures 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 Necessary Ventures
✨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 Necessary Ventures 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.
