Software Engineer; Runtime Platform, Robot Software

Requirements

• Software Development
  
  Skills:
  
  Proficiency developing high-performance embedded Linux systems software in C++. Demonstrated ability to manage the complete software development lifecycle from ideation through delivery & optimization
• Proficiency with performance profiling tools and techniques for identifying and resolving system bottlenecks
• Proven track record of methodical experiment evaluation
• Strong technical background on OS scheduling, computer architecture (memory hierarchy, CPU caches, context switches, …), and thread synchronisation
• (Desirable) Familiarity with Nvidia performance tools such as NV NSight, NV Lumos and tegrastat
• (Desirable) Familiarity with observability tools such as Grafana (logs, metrics, traces), Databricks, Datadog
• (Desirable) Familiarity with QNX and Momentics is a plus
• We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply

• The Robot Software team is responsible for the software that runs on our internal fleet of vehicles to enable autonomous driving and collect data for training new driving models
• You will join a motivated and talented team of engineers to deliver a reliable, stable and flexible software stack to support on-road experimentation by our model developers and scientists
• Your work will enable these teams to iterate quickly and gather the data they need to improve our autonomous driving capabilities and support new product features, which is critical to the success of Wayve’s mission
• The Runtime Platform team equips all Wayve teams with the observability, profiling tools, and infrastructure needed to understand and optimise software performance across our development fleet
• We work closely with teams to investigate issues, reduce bottlenecks, and promote best practices
• Our work enhances core onboard components to ensure efficient use of compute and a solid foundation for running model experiments at scale
• We also provide the tooling and infrastructure needed to quickly detect, diagnose, and address performance regressions, helping teams move faster with greater confidence
• 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 prioritize optimization efforts based on impact
• Optimize critical code paths and algorithms to improve runtime efficiency, reduce latency, and enhance overall system performance