Job Title: Software Development Engineer – Device Platform Engineer
Location: Vancouver, BC (Onsite)
Duration: FTE/Perm
A reasonable, good faith estimate of the minimum and maximum for this position is CAD125K/year to CAD140K/year with benefits
Job Description:
About the Job
Build and maintain the core platform software that powers vision-guided robotic systems. This is a low-level systems engineering role - not web or app development - focused on bridging hardware and software, automating deployments, and ensuring high uptime from the lab to the factory floor.
Key Responsibilities
- Platform Architecture: Write platform tools in Python and C++ to automate system deployment, provisioning, upgrades, and backups across distributed robots.
- Hardware Integration: Connect, containerize (via Docker), and validate hardware components, including vision cameras, NVIDIA GPUs, network devices, and robot controllers.
- Linux Configuration: Build and manage OS-level automation using Ubuntu auto-install, cloud-init, and custom image creation for rapid scaling.
- Reliability & Uptime: Build automated health checks, self-healing workflows, and backup/restore mechanisms to minimize downtime.
- Tooling & Optimization: Create internal tools that reduce setup time by 30%+, utilizing AI-assisted diagnostics and performance profiling.
- Collaboration: Partner with robotics, computer vision, and infrastructure teams across the full technology stack.
Requirements
- Experience: 3+ years in platform engineering, systems programming, or infrastructure-level software development.
- Programming: Strong proficiency in Python and C++.
- OS & Containers: Deep hands-on experience with Linux (Ubuntu), system imaging/provisioning tools (cloud-init, auto-install), and Docker.
- Hardware Interfacing: Proven experience integrating physical components (GPUs, cameras, controllers, network devices) with software layers.
- Operations: Solid foundation in DevOps practices, CI/CD pipelines, and automated system testing.
Preferred Qualifications
- Hands-on work with NVIDIA GPU acceleration inside Docker containers.
- Experience with industrial camera interfaces, Power over Ethernet (PoE), and network peripheral configurations.
- Advanced Linux systems knowledge: SELinux, OverlayFS/SquashFS, and kernel/performance profiling tools.
- Familiarity with AWS cloud infrastructure and distributed architecture.
- Interest in deploying predictive maintenance and AI-driven monitoring systems.
