Senior ML Engineer - Real-Time On-Vehicle AI

OpenDigital Limited; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Wayve is seeking a Senior Machine Learning Engineer to lead production-ready model releases for OEM engagements. This role blends hands-on deep learning with system-level optimization, ensuring models meet strict on-vehicle runtime constraints and performance targets.

You will collaborate with inference and performance teams to deliver scalable, real-time AI solutions and iterate quickly from training to deployment while maintaining a delivery-focused mindset.

Qualifications

• Proven experience improving performance in production systems with tight constraints (latency, memory, power, or cost).
• Strong hands-on experience training and iterating on deep learning models in PyTorch.
• Familiar with stack/toolchain such as TensorRT, CUDA, QNN, Triton, OpenCL.
• Comfort operating from high-level model behavior to low-level runtime execution.

Responsibilities

• Own end-to-end delivery of model releases from requirements through deployment.
• Train and iterate on PyTorch models with an experimental approach.
• Debug and improve model performance, identify regressions, and propose fixes.
• Apply optimisation techniques and understand trade-offs in production.
• Collaborate with ML and performance engineering teams to hand off models.
• Communicate delivery timelines and readjust priorities as needed.

Skills

PyTorch training

Model optimisation

Latency awareness

Cross-functional collaboration

System delivery

Engineering fundamentals

Experimentation

Tools

TensorRT

CUDA

QNN

Triton

OpenCL

Job description

Wayve is seeking a Senior Machine Learning Engineer to lead production-ready model releases for OEM engagements. This role blends hands-on deep learning with system-level optimization, ensuring models meet strict on-vehicle runtime constraints and performance targets.

You will collaborate with inference and performance teams to deliver scalable, real-time AI solutions and iterate quickly from training to deployment while maintaining a delivery-focused mindset.