Summary
What you’ll impact
The Senior AI/Computer Vision Engineer will lead the design, development, optimization, and deployment of AI models and inference pipelines for cloud and edge platforms to advance wildfire detection and related vision capabilities. This hands‑on technical leadership role involves creating computer vision and speech recognition models, optimizing them for edge devices, and mentoring junior engineers.
Responsibilities
What you'll do
- Design and implement cloud/edge AI architectures for real-time computer vision applications.
- Develop computer vision models for:
- Wildfire smoke detection
- Vegetation detection and classification
- Asset detection (e.g., power lines, utility poles, buildings, roads)
- Scene understanding and semantic segmentation
- Spatial reasoning, including estimating distances and relationships between detected objects and nearby assets
- Develop speech recognition models for fire-related radio communications
- Build lightweight detection, segmentation, classification, and temporal reasoning models for real-time inference.
- Port and optimize deep learning models for ARM64, CUDA, TensorRT, ONNX, and NVIDIA Jetson platforms.
- Build and optimize both cloud and edge inference pipelines for RGB, NIR, PTZ, and multi-camera systems.
- Develop hybrid edge-cloud AI workflows that balance latency, bandwidth, and compute efficiency.
- Improve inference latency, throughput, memory usage, and power efficiency.
- Lead model compression efforts, including quantization, pruning, and knowledge distillation.
- Design deployment, monitoring, OTA update, and observability capabilities for edge AI systems.
- Collaborate closely with AI researchers, software engineers, hardware engineers, data engineers, and product teams.
- Mentor junior engineers and establish best practices for edge AI and computer vision development.
Requirements
What you’ll bring
- MS or PhD in Computer Science, Electrical Engineering, Robotics, or a related field.
- 5+ years of industry experience in computer vision or machine learning.
- Strong experience with PyTorch and modern deep learning architectures.
- Experience deploying AI models to edge devices such as NVIDIA Jetson, embedded GPUs, or similar platforms.
- Strong understanding of CUDA, TensorRT, ONNX, model optimization, and inference acceleration.
- Experience with one or more of the following:
- Object detection
- Semantic or instance segmentation
- Image classification
- Video understanding
- Multi-object tracking
- Depth estimation or 3D computer vision
- Speech recognition
- Strong Python and C++ programming skills.