Senior Embedded Runtime Engineer - Performance Profiling

Artificial Intelligence Jobs

Greater London

Hybrid

GBP 90,000 - 150,000

Full time

14 days+
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Job summary

Wayve in London is seeking a Software Engineer to join the Robot Software and Runtime Platform teams, delivering a reliable, high-performance embedded Linux stack to support on-road experimentation and autonomous driving development. You will diagnose latency, instrument code, integrate profiling tools, and collaborate with model developers and scientists to improve runtime efficiency and data collection.

This is a hybrid role with in-office and home work, with core hours to align with project

Qualifications

  • Proficiency developing high-performance embedded Linux systems software in C++.
  • Performance profiling tools to identify bottlenecks and optimise systems.
  • Methodical approach to experiment evaluation and validation.
  • Strong understanding of OS scheduling, memory hierarchy, CPU caches and concurrency.

Responsibilities

  • Investigate latency and performance bottlenecks across the entire software stack.
  • Instrument code with profiling tools and metrics to collect performance data and identify optimization opportunities.
  • Integrate and evaluate new profiling and instrumentation tools to benchmark system performance and establish baselines.
  • Collaborate with multiple teams to understand their performance pain points and prioritise optimization efforts based on impact.
  • Optimize critical code paths and algorithms to improve runtime efficiency, reduce latency, and enhance overall system performance.

Skills

C++ embedded
Profiling tools
Experimentation
OS scheduling

Tools

NV NSight
NV Lumos
tegrastat
Grafana
Datadog
QNX
Momentics

Job description

Wayve in London is seeking a Software Engineer to join the Robot Software and Runtime Platform teams, delivering a reliable, high-performance embedded Linux stack to support on-road experimentation and autonomous driving development. You will diagnose latency, instrument code, integrate profiling tools, and collaborate with model developers and scientists to improve runtime efficiency and data collection.

This is a hybrid role with in-office and home work, with core hours to align with project

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