Software Engineer - Performance Optimization

Applied Intuition
Mountain View, California, United States$199k – $265kPosted 24 April 2026

Job Description

About Applied Intuition Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co . We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. About Applied Intuition Applied Intuition is a Tier 1 vehicle software supplier that accelerates the adoption of safe and intelligent machines worldwide. Founded in 2017, Applied Intuition delivers the definitive ADAS/AD product toolchain and a world-class vehicle platform to help customers shorten time to market, build industry-leading products, and create next-generation consumer experiences. 18 of the top 20 global automakers trust Applied Intuition’s solutions to drive the production of modern vehicles. Applied Intuition serves the automotive, trucking, construction, mining, agriculture, and defense industries and is headquartered in Mountain View, CA, with offices in Ann Arbor and Detroit, MI, Washington, D.C., Munich, Stockholm, Seoul, and Tokyo. Learn more at https://appliedintuition.com. About the role We are looking for a Software Engineer with deep experience in optimizing the application-layer software for embedded systems. In this role, you will work across multiple components of an autonomous driving software stack deployed on embedded compute platforms. You will be responsible for fitting a complex software stack into tight compute budgets while maintaining algorithmic performance, analyzing runtime behavior, and ensuring efficient concurrent execution of multiple applications. At Applied Intuition, you will: 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 We're looking for someone who has: 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 have: Exposure to ML models and runtime frameworks (PyTorch, ONNX, TensorRT) Experience with memory-constrained deployments and concur ... (truncated, view full listing at source)
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