ML Runtime Optimization Engineer
Applied Intuitionover 1 year ago
Sunnyvale, CA, USAMid Level
H1B Sponsor
Base Salary
$159k - $199k/yr
Responsibilities
- Drive ML performance optimization across technologies for on-road and off-road ADAS and autonomous-driving stacks targeting embedded compute platforms.
- Develop compute-usage strategies to improve model-inference efficiency and latency for customer-selected compute boards.
- Apply model pruning and quantization to support deployment on memory-constrained platforms.
- Collaborate with ML engineers and software developers to identify efficient model-architecture solutions.
- Establish profiling methodologies on target embedded compute platforms and identify performance bottlenecks during stack integration.
Requirements
- Bachelor’s degree in Electrical Engineering or Computer Science, or a B.Sc. in Computer Science, Mathematics, Physics, or a related field.
- At least 3 years of experience with ML accelerators, GPUs, CPUs, SoC architecture, and micro-architecture.
- Strong software development skills focused on embedded programming.
- Experience profiling and optimizing model performance on embedded compute platforms.
- Experience working with deep-learning frameworks such as PyTorch, JAX, and ONNX.
- Preferred: M.Sc. or PhD in an ML-related area.
- Preferred: experience building an ML optimization framework from scratch.
- Preferred: experience deploying ML solutions to embedded chips for real-time robotics applications.
Benefits
- Base salary, equity, and benefits including health, dental, vision, life, and disability insurance.
- 401(k) retirement benefits with employer match, learning and wellness stipends, and paid time off.
- Primarily in-office work five days per week, with occasional remote-work flexibility.
- Full-time position.