Embedded Automotive Platforms, Staff Software Engineer at Wayve
At a glance
- Company
- Wayve
- Location
- United Kingdom
- Work arrangement
- On-site
- Employment type
- Full-time
- Experience
- 5–10 years
- Working hours
- 40 hours
- Visa sponsorship
- Offered, according to the listing
- Posted
- Added to The Stack
✨ The Stack take on this Wayve job
- 🏢 Wayve has 44 jobs here, 42 with visa sponsorship see all
- 📊 56 Software Engineer jobs in the United Kingdom in the last 60 days see them
- 🛂 Skilled Worker visa. Standard minimum salary: £41,700 a year guide
- 💡 No salary shown. Ask Wayve if it is above the minimum.
Updated 2026-11-05, from our data and official sources.
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About the role
Responsibilities:
- Design, implement and maintain embedded software that connects the AI Driver to vehicle platforms and onboard systems.
- Define and evolve interfaces between AI modules, vehicle software and platform-specific components (inference integration, navigation, localisation, odometry, motion control, HMI).
- Take features from design through implementation, testing (simulation, HIL), vehicle integration and release.
- Diagnose and resolve integration issues using logs, simulation, hardware-in-the-loop and on-vehicle testing; optimise for resource-constrained environments.
- Contribute to architecture, code reviews and engineering practices to maximise reuse across vehicle platforms and programmes.
- Collaborate closely with autonomy, hardware, product and vehicle engineering teams to clarify requirements and unblock deliveries.
Requirements:
- Degree in Computer Science, Electrical or Automotive Engineering (or equivalent) with substantial embedded systems experience.
- Strong modern C++ skills and proven experience on Linux-based embedded platforms (QNX experience advantageous).
- Familiarity with Nvidia/Qualcomm automotive platforms, inference integration and performance-constrained systems.
- Experience with vehicle communication protocols, hardware-in-the-loop testing, simulation and vehicle integration workflows.
- Excellent debugging skills across system boundaries, plus focus on observability, resilience and software quality.
- Experience with automotive processes or safety-relevant development (e.g., ASPICE) is a plus.
Responsibilities
Design, build, and maintain embedded software that integrates the AI Driver with vehicle platforms and onboard systems. Collaborate across autonomy, hardware, and product teams to deliver robust, testable software for real-world vehicle deployment.
Requirements
Requires a degree in Computer Science, Electrical, or Automotive Engineering with strong experience in modern C++ and embedded systems. Candidates must demonstrate proficiency in Linux-based platforms and the ability to diagnose complex issues across system boundaries.
Key skills
- C++ — in 133 other listings on The Stack
- Embedded software — in 5 other listings on The Stack
- Linux — in 151 other listings on The Stack
- QNX — in 8 other listings on The Stack
- System architecture — in 171 other listings on The Stack
- API design — in 192 other listings on The Stack
- Software testing — in 35 other listings on The Stack
- Debugging — in 82 other listings on The Stack
- Code review
- Nvidia platforms
- Qualcomm platforms
- Autonomous driving
- Machine learning inference
- Hardware-in-the-loop testing
- Vehicle communication protocols
- ASPICE
On The Stack (as of 2026-11-05)
Frequently asked questions about Wayve
Is this Embedded Automotive Platforms, Staff Software Engineer role still open?
It was posted on 2026-11-04 and is listed as active on The Stack. Confirm on Wayve's application page before applying.
Does Wayve offer visa sponsorship?
This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on Wayve's site.
Where is this role located?
The listing gives the location as United Kingdom. It is marked on-site.
What skills does this role ask for?
The listing names: C++, Embedded software, Linux, QNX, System architecture, API design, Software testing, Debugging, Code review, Nvidia platforms, Qualcomm platforms, Autonomous driving, Machine learning inference, Hardware-in-the-loop testing, Vehicle communication protocols, ASPICE.