Applied Intuition is looking to optimize ML models and deploy them on production-grade embedded runtime environments to accelerate the global adoption of safe, AI-driven machines.
Requirements
- 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.)
- Bachelors in Electrical Engineering or Computer Science, OR B.Sc. in Computer Science, Mathematics, Physics or a related field
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
Other
- Bachelors in Electrical Engineering or Computer Science, OR B.Sc. in Computer Science, Mathematics, Physics or a related field
- 5+ years of experience
- In-office work with occasional remote work
- Must be eligible to work in the location listed
- Comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match
