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Applied Intuition

Robot type
Autonomous Vehicle · Robot AI and Software
Location
SunnyvaleCaliforniaUSA
Job type
Artificial Intelligence
Posted
Mar 4, 2025
Salary
$216,800–$318,000 a year
Full-time

ML Runtime Optimization Engineer

Job description

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.;

Job responsibilities

  • 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
  • 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

Job requirements

  • Bachelors in Electrical Engineering or Computer Science, OR B.Sc. in Computer Science, Mathematics, Physics or a related field
  • 3+ 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.)
Apply on Applied Intuition websiteSave

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