Job descriptionWe are looking for a hands-on
Embedded Software Engineer with proven experience
integrating and optimizing multiple AI applications on NVIDIA edge platforms for deployment in industrial machine environments. You will work at the intersection of real-time embedded systems and AI inference - tackling technically demanding problems under strict performance and safety constraints - helping bring intelligent perception capabilities to production-grade hardware. This is a high-impact role within a collaborative engineering team where your contributions directly shape the behavior of intelligent machines in the field.
Location This position is based
in Peoria, Illinois. Candidates must be authorized to work in the
United States.
Responsibilities - Integrate and co-optimize multiple applications running concurrently on NVIDIA Thor IGX and Orin NX platforms
- Implement and tune object detection, personnel classification, and segmentation algorithms for edge deployment
- Port and adapt AI/ML inference workloads to NVIDIA hardware using TensorRT, CUDA, and related toolchains
- Collaborate with safety and perception teams to validate detection performance on target hardware
- Support system-level testing, benchmarking, and performance profiling
- Contribute to CI/CD pipelines and validation cycles for embedded AI software
Required Qualifications - 5+ years of experience in embedded software development with a focus on AI/ML inference on edge hardware
- Direct, hands-on experience with NVIDIA Jetson, Orin NX, and/or Thor IGX platforms
- Proficiency with TensorRT, DeepStream, CUDA, and the broader NVIDIA SDK toolchain
- Demonstrated experience running and optimizing multiple concurrent applications on a single compute platform
- Strong background in computer vision, including object detection, segmentation, and classification
- Ability to operate effectively in fast-paced, resource-constrained programs with aggressive delivery milestones
What Makes You Stand Out - Experience deploying AI workloads in industrial or safety-critical environments
- Familiarity with perception pipeline architecture and sensor fusion
- Knowledge of embedded Linux, real-time operating constraints, and hardware bring-up
- Exposure to model quantization, pruning, or other inference optimization techniques
