Wayve interview, decoded.

The four stages are Wayve's own, from its machine learning engineer ads published in July 2026. The round contents come from Glassdoor reviews and candidate accounts. Glassdoor puts the average at about 24 days; one account puts it at two weeks.

Wayve's ads close with a warning that it is not a settled company, and the final interview checks you are fine with that.

• Ownership where the playbook is still being written
• Pragmatic, measurable progress on real driving performance
• End-to-end work across data, training, evaluation and iteration
• Care for safety on real roads
• Mission alignment with embodied AI

Candidates describe the formats in more detail than the questions. The reported ones are below; the technical themes come from the reports and the ads.

Machine learning candidates describe one hour with a Google Colab notebook that trains a convolutional network and has errors to find and fix. Nobody has published the bugs themselves, and they will change. These are the faults a broken training loop usually contains, in the order worth checking, so you have a routine instead of a guess.

• Does it run at all?: Read the first stack trace carefully. Shape mismatches between the last convolution and the first linear layer, and tensors on different devices (one on the GPU, one on the CPU), are the usual crashes.
• Is the data right?: Show a few images with their labels before touching the model. Look for labels that do not match, a missing or doubled normalisation, channels in the wrong order, and a train set that is not shuffled.
• Is the loss right for the output?: Cross-entropy in PyTorch expects raw scores and class indices; a softmax before it, or one-hot targets, quietly breaks training. Check the loss is averaged the way you think.
• Is the training step in the right order?: zero_grad, forward, loss, backward, step. A missing zero_grad lets gradients pile up; a step before backward does nothing useful.
• Are train and eval modes switched?: model.train() for training and model.eval() with torch.no_grad() for evaluation, or dropout and batch norm behave wrongly and the validation numbers lie.
• Can it overfit one batch?: Train on a single small batch. If the loss will not go near zero, the bug is in the model or the loop, not the data size. Then check the learning rate and the optimizer's parameter list.

Say what each bug did to training as you fix it. The round is as much about how you reason through a broken run as about the fixes.

Levels.fyi puts the median software engineer package at Wayve in Greater London at £129K, updated in August 2026 and read through search results on 23 September 2026. Wayve's UK ads mostly carry no range; its Sunnyvale ads do, from $176,500 for a fleet management software engineer to $311,850 to $389,400 for a senior machine learning engineer, each with equity. The ads also promise salaries benchmarked yearly, relocation and visa sponsorship where it applies. Calibrd benchmarks the pay on any posting you scan against its level and location.

1. 01Break a PyTorch training loop on purpose, then fix it against a clock. Run through data, loss, step order, modes and overfitting one batch until it is a habit.
2. 02Revise data structures and algorithms for a live hour. The competency interviews include one for every engineering role.
3. 03Prepare a design for driving data at scale: ingesting petabytes, letting one corrupt segment fail without blocking the rest, and making it searchable for rare events.
4. 04Practise mining a dataset for rare events, like the reported emergency brake question: what signals you would use, how you would check the results.
5. 05Read about end-to-end driving before the values round and have a view on it. The final hour is about the mission.

This guide covers Wayve's machine learning and software engineering loops. Hardware, fleet operations and field roles run a different process. For management and leadership roles the loop is similar but the bar shifts to people, delivery and strategy, so pair it with the leadership interview prep hub. The bar for your exact role comes from the role-by-role guides, and the prep that actually transfers is spoken, so run a mock interview before the real one.

• 01Wayve careers, Machine Learning Engineer, ADAS (London)the four-stage loop (30-minute recruiter screen, two hours of programming and system design, one hour of domain deep dive, one hour on mission and values), relocation and visa support, hybrid working; published 7 July 2026
• 02Glassdoor, Wayve machine learning engineer interviewsthe one-hour PyTorch debugging interview on a Colab notebook training a CNN, the deep learning case study, the one-hour programming round, and the emergency brake data question; read through search snippets
• 03Glassdoor UK, Wayve software engineer interviewsrecruiter, technical phone screen, hiring manager, then an onsite with system design and two behavioural rounds; read through search snippets
• 04TechPrep, Wayve's interview processthe stage durations for software engineers: a live coding hour with a technical lead, an architecture discussion, and a final alignment interview often with a director of engineering
• 05Wayve careers, SWE, Data Ingestionover 500,000 hours of driving data, hundreds of petabytes, and pipelines where one bad segment must not block others; read 23 September 2026
• 06Wayve job board193 open roles on 23 September 2026, about 112 in engineering and science, mostly Sunnyvale and London, with posted US ranges
• 07Levels.fyi, Wayve software engineer, Greater Londonthe £129K median, updated 25 August 2026, read through search results on 23 September 2026
• 08Wayve, Series D announcementthe $1.2B Series D at $8.6B post-money on 25 February 2026, led by Eclipse, Balderton and SoftBank Vision Fund 2, with Microsoft, NVIDIA, Uber, Mercedes-Benz, Nissan and Stellantis
• 09Bloomberg, Uber and Wayve launch a robotaxi service in Londonthe September 2026 launch with a safety driver on board

Interview processes change. This reflects widely-reported and sourced conditions as of 2026 — confirm specifics with your recruiter, and treat it as a map rather than a guarantee.