Sr. AI / Embedded ML Engineer
Tasks
- Apply quantization pruning and distillation
- Build data ingestion pipelines from sensors
- Clean label synchronize and store raw sensor data
- Collaborate with hardware firmware and data teams
- Deploy ML inference on microcontrollers
- Design hybrid edge and LLM architectures
- Develop and train machine learning models
- Document design decisions and benchmarks
- Integrate ML inference into embedded firmware
- Integrate ML into real time systems
- Optimize models for edge deployment
- Profile and optimize memory and power
- Provide technical support for system integration and field testing
- Route tasks between edge inference and LLM components
Perks/Benefits
- Continuous learning and development
- Fair and honest workplace
- Financial solutions for the future
- Health and wellness care options
- Optional Legal Services US Only
- Paid Holidays
- Paid time off
- Sustainability focus
Skills/Tech-stack
ARM Cortex | ARM Cortex-M | Accelerometer | Automl | Barometer | C# | C++ | Cortex-M | DSP | Data Processing | Deep learning | Edge Impulse | Executorch | FFT | FreeRTOS | Gyroscope | IMU | Knowledge Distillation | Machine Learning | Microphone | Model Optimization | ONNX Runtime | Pruning | PyTorch | Python | Quantization | RISC-V | RTOS | Real Time | Real-time Systems | Rust | Scikit-learn | Sensor Data Processing | Sensor data | Signal Processing | TensorFlow | TensorFlow Lite | TensorFlow Lite Micro | Time Systems | TinyML | Zephyr
Education
N/A
Roles
AI Engineer | Embedded ML Engineer | Engineer | ML Engineer | Senior AI Engineer
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