Applied Intuition is looking to optimize the application-layer software for embedded systems within autonomous driving software stacks, ensuring efficient resource utilization and performance on constrained compute platforms.
Requirements
- Strong C++ development skills with a focus on runtime performance
- Experience profiling CPU, GPU, and memory usage performance on constrained compute
- Proven ability to debug complex runtime issues and resolve onboard resource contention
- Exposure to ML models and runtime frameworks (PyTorch, ONNX, TensorRT)
- Experience with memory-constrained deployments and concurrent scheduling
- Prior experience with autonomous driving software stacks
- Scripting experience for performance profiling and automation
Responsibilities
- Analyze runtime performance of the application layer and identify potential resource contentions
- Optimize compute usage to fit within embedded platform constraints without sacrificing algorithm accuracy or latency
- Profile and tune performance on embedded targets under real-world operating conditions
- Collaborate closely with ML runtime optimization engineers to ensure smooth model inference execution within the stack
- Proactively design for contention avoidance and thread safety through code reviews and software architecture reviews; propose single threaded lock-free approaches where appropriate
- Deploy and validate production code on QNX, Linux-based embedded, or similar RTOS platforms
- Contribute to improving system-wide runtime, latency, and performance monitoring tools
Other
- We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week.
- Bachelors or Masters in Electrical Engineering or Computer Science or a related field
- 5+ years of experience in software development
- Applicants will be required to be fully vaccinated against COVID-19 upon commencing employment.
- Applied Intuition is an equal opportunity employer and federal contractor or subcontractor.
