ML Runtime Optimization Engineer

Applied Intuition • Mountain View, CA

161d • $159,053 - $199,295 • Remote

About The Position

Applied Intuition is a vehicle software supplier that accelerates the adoption of safe and intelligent machines worldwide. Founded in 2017, Applied Intuition delivers the AI-powered ADAS/AD toolchain, vehicle platform, and autonomy stack to help customers shorten time to market, build high-quality systems, 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 San Diego, CA, Ft. Walton Beach, FL, Ann Arbor and Detroit, MI, Washington, D.C., Stuttgart, Munich, Stockholm, Seoul, and Tokyo. Learn more at appliedintuition.com. Please note that we are an in-office company, which means the expectation is that you would come in to your Applied Intuition office 5 days a week. (Note this is not applicable for EpiSci job roles.)

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.)

Nice To Haves

• 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

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
• 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

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
• Paid time off

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Industry: Publishing Industries
• Education Level: Bachelor's degree
• Number of Employees: 501-1,000 employees