Embedded Software Engineer

Lawrence Harvey

$90K — $110K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 2-4 years of experience in embedded software engineering
  • Proficient in Python and C++ programming languages
  • Experience with Linux operating systems
  • Familiar with edge GPU technologies, particularly NVIDIA
  • Knowledge of Ethernet/Networking concepts
  • Understanding of Docker for containerization

Responsibilities

  • Build resilient software for edge deployment of high-throughput image data
  • Engineer deployment systems for seamless installation in diverse environments
  • Enhance reliability and maintainability of 24/7 unattended services
  • Collaborate with ML engineers to optimize models for edge hardware
  • Conduct troubleshooting during field visits and incorporate feedback for software improvement

Benefits

  • Work on impactful projects for Fortune 500 manufacturers
  • Join a small, dynamic, and gritty team
  • High-ownership role with direct influence on product success
  • Opportunity to solve real-world manufacturing problems
  • Potential for occasional travel to diverse manufacturing sites
Full Job Description
We are looking for an Embedded Software Engineer with 2-4 years of experience to help scale our edge deployment platform for inline manufacturing inspection. You'll be the "rubber meets the road" engineer - taking our proven AI-powered inspection system and making it reliable, scalable, and deployable anywhere in the world without heavy on-site support. This is a high-ownership role on a small, gritty team solving real problems for Fortune 500 manufacturers. If you're tired of building demos and want to ship software that runs in real production environments on factory floors, this is for you.

What will you be doing?
  • Building resilient edge software that reliably streams high-throughput image data through edge GPUs and runs inference services in diverse customer network environments
  • Engineering deployment and configuration systems so the platform can be shipped in a box, installed on a production line anywhere in the world, and work flawlessly without heavy on-site support
  • Improving reliability, observability, and recoverability of unattended services running 24/7 on factory floors
  • Partnering with ML engineers to integrate and accelerate models on edge hardware
  • Troubleshooting issues in the field during occasional on-site visits (1-2 trips per quarter) and feeding learnings back into more robust software

Tech stack

Python, C++, Linux, Edge GPUs (NVIDIA), Ethernet/Networking, Docker

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