Type
Full-time
Work mode
On-site
Level
Staff
Industry
Engineering / Mechanical
Salary
Thương lượng
Location
Not Available, Hồ Chí Minh, Hồ Chí Minh
Overview
- Develop and maintain Linux-based firmware for custom embedded hardware platforms.
- Work on bootloaders (U-Boot, Barebox, etc.), kernel configuration, and device drivers — including camera (V4L2, MIPI CSI) and sensor drivers.
- Integrate and optimize open-source software stacks for secure and real-time applications.
- Build and tune on-device camera and computer-vision pipelines (GStreamer, OpenCV, V4L2) for streaming, capture, and real-time analytics.
- Deploy and accelerate AI/ML models on edge hardware — quantize, prune, and integrate models via TensorRT, OpenVINO, TensorFlow Lite, ONNX Runtime, or vendor NPUs (e.g. Hailo, NVIDIA Jetson, Rockchip NPU, Ambarella).
- Enable secure OTA updates, encryption, key management, and remote diagnostics.
- Collaborate closely with hardware, AI, security, and backend teams to ship complete solutions.
- Analyze and resolve performance, stability, and integration issues across the camera-to-inference path.
- Support manufacturing and QA teams with tooling and debugging aids.
- Lead the embedded Linux team technically — set coding standards, run design and code reviews, mentor engineers, and own technical decisions across the product line.
- Your Skills and Experience
- Skills & Experience:
- 5+ years of experience in embedded Linux development, with at least 2 years at senior level.
- Minimum 2 years of experience as a technical lead — driving architecture decisions, owning a product or major subsystem end-to-end, running code reviews, and mentoring 2+ engineers.
- Strong knowledge of kernel and user-space programming, especially in C/C++.
- Hands-on experience with bootloaders, device trees, Yocto/OpenEmbedded or Buildroot.
- Hands-on experience with camera integration on Linux: V4L2, MIPI CSI, ISP tuning, GStreamer pipelines, and at least one of OpenCV or a comparable CV framework.
- Working experience deploying AI/ML inference on edge devices — model conversion, quantization, and runtime integration with at least one of TensorRT, OpenVINO, TensorFlow Lite, ONNX Runtime, or a vendor NPU SDK.
- Familiarity with secure boot, trusted execution environments (TEE), and encryption fundamentals.
- Solid grasp of low-level interfaces: SPI, I2C, UART, GPIO, USB, Ethernet, MIPI.
- Ability to read schematics and collaborate effectively with hardware engineers.
- Comfortable with Git, CI/CD pipelines, and Linux shell scripting.
- Strong written and spoken English for cross-office collaboration with US and Australia teams.
- Nice to Have / Preferred
- Experience with HSMs, secure elements, or PKI in embedded contexts.
- Experience with BLE, Wi-Fi, NFC, or UWB stack integration.
- Hands-on experience with specific edge AI platforms: Rockchip NPU, Qualcomm QCS.
- Experience with face detection / recognition pipelines, person detection, or other computer-vision use cases relevant to physical access control.
- Familiarity with model optimization techniques: INT8 quantization, pruning, knowledge distillation, layer fusion.
- Experience formally line-managing engineers (1:1s, performance reviews, growth planning).
- Success Profile:
- The ideal candidate is:
- Deeply technical and hands-on, comfortable moving between kernel, driver, and AI/inference layers.
- A natural technical leader who mentors engineers and sets high engineering standards.
- Methodical about security and reliability in embedded systems.
- Able to own architecture decisions end-to-end and drive them to production.
- Comfortable working across regions and functions in a global environment (Vietnam, US, Australia).
- Why You'll Love Working Here
Summary of facts from the official posting. View original ↗
Interested in this role?
You'll be taken to the employer's official application page.
Apply on official site ↗
Is this your business?
Claim this page, request edits or removal
→