AI and Machine Learning Innovator for Edge Devices

19 hours ago

Shimoga, Karnataka, India beBeeEmbeddedSoftware Full time US$ 1,20,000 - US$ 1,80,000
Embedded Software Engineer for AI and ML Workloads

We are seeking a talented Embedded Software Engineer to join our team.

  • Main Responsibilities:
  • Design, implement, and optimize embedded software solutions for AI/ML workloads on NVIDIA, Xilinx, and Microchip platforms.
  • Work under the Head of Engineering and CTO, contributing to managing development kits and system integration.
  • Develop the embedded software stack that powers edge intelligence solutions in space.

This is a remote-first position, requiring excellent technical communication to collaborate across global teams and partner satellite operators.

Key Skills:

  • Linux Expertise: Configure and manage Linux environments (Ubuntu and custom distributions), including kernel modules, system tuning, cross-compilation, and deployment scripts.
  • Ai/Ml Optimization and Testing on the Edge: Integrate ml models into embedded systems; collaborate with Data Science teams to enable deployment on resource-constrained hardware.
  • Low-Level Engineering: Work with c, c++, and python to build and optimize drivers, os-level services, and low-level libraries for ml acceleration.
  • Containerization: Develop, test, and optimize dockerized applications for ai/ml workloads, ensuring portability, efficiency, and compliance with partner satellite company requirements.
  • Embedded Systems Ownership: Manage and maintain Edge Ai development kits (nvidia jetson, xilinx platforms, etc.), ensuring smooth workflows for in-orbit simulation and testing.
  • Hardware Acceleration: Leverage cuda, cudnn, tensorrt (nvidia) and vitis ai / opencl (xilinx) to accelerate workloads.
  • Systems Integration: Interface with satellite payload hardware and ground systems; debug integration issues across hardware, os, and application layers.
  • Ci/cd for Edge: Develop pipelines for automated testing and deployment of embedded software builds.
  • Documentation & Support: Maintain clear documentation of configurations, workflows, and deployment practices; support partner satellite companies in deploying our dockerized products.
  • Version Control & Collaboration: Git/github, code review best practices, and working in distributed development teams.

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