ML Runtime Optimization Engineer

Responsibilities

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

Requirements

• Bachelor's degree in Electrical Engineering, 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 a focus on embedded programming
• Experience profiling and optimizing model performance on embedded compute platforms
• Experience 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
• Experience deploying ML solutions to embedded chips for real-time robotics applications

About the Company

Applied Intuition is powering the future of physical AI, creating the digital infrastructure needed to bring intelligence to every moving machine on the planet, serving industries like automotive, defense, and agriculture.