Application Engineer - Low Power Edge Inference (DIB Focus)
Tasks
- Deploy and optimize ML models for edge inference
- Develop reference designs and integration guides
- Diagnose issues across SPI I2C GPIO interfaces and components
- Enable prototyping using SDKs and toolchains
- Identify tooling documentation and product capability gaps
- Integrate sensors and data pipelines into SoC
- Perform hardware software debugging using lab instrumentation
- Profile and improve latency energy per inference memory usage and accuracy
- Serve as technical interface between engineering teams and defense customers
- Support board bring up and validation in labs and customer environments
- Support demos pilots and integration efforts
- Support system bring up and SoC integration
- Translate customer requirements into engineering feedback
- Travel for on site bring up debugging and deployment
- Troubleshoot hardware firmware and ML inference pipeline issues
Perks/Benefits
Skills/Tech-stack
ADC | Accuracy Optimization | Build systems | C# | C++ | Cross-compilation | Embedded Systems | Energy optimization | Firmware | GPIO | Git | Hardware debugging | I2C | Latency | Linux | Logic analyzer | ML inference | Machine Learning | Memory Optimization | Multimeter | Oscilloscope | Profiling | PyTorch | Python | SDK | SPI | Sensor Integration | Signal Processing | TensorFlow Lite | TinyML | Toolchains | UART | Version control
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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