Applied Intuition is looking to optimize ML model performance on production-grade embedded runtime environments.
Requirements
- 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.)
- 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
- 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
- In-office work 5 days a week
- 401k retirement benefits with employer match, learning and wellness stipends, and paid time off
