ML Runtime Optimization Engineer
Sunnyvale, CA$159.1k - $199.3kfull-timePosted 10mo ago
About the role
Software engineer role focused on optimizing ML models and deploying them on production-grade embedded runtime environments for on-road and off-road ADAS/AD stacks, across multiple ML framework stacks and embedded compute platforms.
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
Qualifications
- Bachelors in Electrical Engineering or Computer Science, OR B.Sc. in 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 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.)
Skills
Required:ML model optimizationEmbedded programmingModel pruning and quantizationDL frameworks
Benefits
- base salary and equity
- comprehensive health, dental, vision, life and disability insurance
- 401k retirement benefits with employer match
- learning and wellness stipends
- paid time off
Last updated May 26, 2026. Interested? Apply on the company site.
About Applied Intuition
Building vehicle intelligence | Applied Intuition
Applied Intuition is the vehicle intelligence company with the tools, Vehicle OS, and autonomy stacks for creating safe, AI-powered vehicle experiences.
AIAutonomy stacksSelf-Driving SystemSimulationTesting & ValidationVehicle IntelligenceVehicle OSai-infrastructure-and-development
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