ML Runtime Optimization Engineer

Responsibilities

• Drive ML performance optimization on multiple technologies for on-road and off-road ADAS / AD stacks targeting deployment on various embedded compute platforms
• Develop compute usage strategies to optimize efficiency and latency of model inference for customer-selected compute boards
• Work on model pruning and quantization to support deployment on memory-constrained platforms
• Collaborate closely with ML engineers and software developers to find and optimize efficient model architecture solutions
• Set up methodologies to profile model performance on target embedded compute platforms and identify performance bottlenecks

Requirements

• Bachelors in Electrical Engineering, Computer Science, Mathematics, Physics, or a related field
• 3+ years of experience with ML accelerators, GPU, CPU, or SoC architecture and micro-architecture
• Strong software development skills with a focus on embedded programming
• Experience profiling and optimizing model performance on embedded compute platforms
• Experience working with deep learning frameworks such as PyTorch, JAX, or ONNX

Preferred Qualifications

• M.Sc or PhD in an ML-related area
• Experience building an ML optimization framework from scratch
• Proven track record of deploying ML solutions to embedded chips for real-time robotics applications

About the Company

Applied Intuition is powering the future of physical AI. We are creating the digital infrastructure needed to bring intelligence to every moving machine on the planet, servicing the automotive, defense, trucking, construction, mining, and agriculture industries.