ML Runtime Optimization Engineer - Lead

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 the role We are looking for a lead software engineer with deep experience in optimizing ML models and deploying them on production-grade embedded runtime environments. You’ll work across the entire ML framework stack (e.g. PyTorch, JAX, ONNX, TensorRT, CUDA, XLA, Triton). At Applied Intuition, you will: Drive ML performance optimization on multiple technologies for on-road and off-road ADAS / AD stacks targeting deployment on a variety of embedded compute platforms Bring technical leadership to the ML model performance optimization team Develop compute usage strategies to optimize efficiency and latency of model inference for compute boards selected by our customers Work on model pruning and quantization, and support deployment on memory constrained platforms Collaborate closely with ML engineers and software developers on technical efforts to find and optimize efficient model architecture solutions Set up methodologies to profile the model performance on target embedded compute platforms and identify performance bottlenecks as part of stack integration We're looking for someone who has: Bachelors in Electrical Engineering or Computer Science, OR B.Sc. in Computer Science, Mathematics, Physics or a related field 5+ years of experience with ML accelerators, GPU, CPU, SoC architecture and micro-architecture Strong software development skills with the focus on embedded programming Experience profiling and optimizing model performance on embedded compute platforms Experience in working with deep learning frameworks (e.g., PyTorch, JAX, ONNX, etc.) Nice to have: M.Sc or PhD in a ML related area Built an ML optimization framework from scratch before Deployed ML solutions to embedded chips for real time robotics applications Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment. Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the posit ... (truncated, view full listing at source)