Software Engineer - Performance Optimization

Applied Intuition•Mountain View, CA

4h•Onsite

About The Position

Applied Intuition is seeking a Software Engineer with deep experience in optimizing application-layer software for embedded systems. This role involves working across multiple components of an autonomous driving software stack deployed on embedded compute platforms. The primary responsibilities include fitting a complex software stack into tight compute budgets while maintaining algorithmic performance, analyzing runtime behavior, and ensuring efficient concurrent execution of multiple applications. The company is a Tier 1 vehicle software supplier accelerating the adoption of safe and intelligent machines worldwide, serving industries such as automotive, trucking, construction, mining, agriculture, and defense. Applied Intuition is an in-office company, with the expectation that employees primarily work from their office 5 days a week, though flexibility for occasional remote work or schedule adjustments is recognized.

Requirements

Bachelors or Masters in Electrical Engineering or Computer Science or a related field
5+ years of experience in software development
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

Nice To Haves

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