Machine Learning Engineer, ADAS
Wayve · London
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Why this job?
Discovery score 55/100, built only from evidence stored with this listing.
- New official employer listing
- Visa sponsorship keyword detected
- Relocation keyword detected
Score components
- Recency (moves as the posting ages)+18
- Official employer source+15
- Visa sponsorship mentioned+7
- Relocation mentioned+6
- Rare role+1
- Company source health+8
Not present on this posting: Salary disclosed、Remote position、Not found on monitored job boards.
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Job description
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!
🛠️ About our Engineering Teams
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.
🧠 Your day-to-day
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.
🧩 What you’ll be working on:
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.
🌱 Not tick