AI Engineer Video

Location: Charlotte, NC

NO C2C, must be in Charlotte NC

Employment Type: Full time

The company Vision team builds GPU accelerated video analytics for real time safety monitoring across large fleets and industrial environments. Our system processes high volume video streams, runs YOLO based detection models, performs temporal tracking and smoothing to reduce false positives, and identifies actionable safety violations. Inference results are published to downstream APIs and integrated with Azure Event Hub, Blob Storage, and cloud monitoring systems.

If you enjoy pushing GPU performance limits, crafting resilient ML pipelines, and building real world safety applications that make an impact, you’ll fit right in.

Responsibilities

• Develop and optimize GPU accelerated video inference pipelines, including batching, stride control, and throughput tuning.
• Implement, evaluate, and improve object detection models (YOLO or similar) and build temporal smoothing/tracking logic for safety event detection.
• Optimize model performance using TensorRT, ONNX, CUDA, and GPU profiling tools to maximize throughput and minimize latency/VRAM usage.
• Build and maintain integrations with event-driven APIs, Azure Event Hub, Blob Storage, and internal services.
• Add robust metrics, logging, telemetry, and fail safe mechanisms for resilient inference jobs.
• Collaborate on dataset curation, labeling, model training, validation, and experiment tracking.
• Support containerized deployments (Docker) and assist with monitoring and scaling production workloads.

Qualifications

Requirements

• 3 years of experience shipping computer vision or machine learning systems to production.
• Strong proficiency in Python and experience with OpenCV, PyTorch, async I/O frameworks, and API integrations.
• Hands on experience with YOLO/Ultralytics or similar object detection frameworks.
• Solid understanding of video processing fundamentals: frame sampling, temporal filtering, confidence thresholds, and multi-camera aggregation.
• Experience optimizing GPU inference performance: batching, stride, TensorRT, CUDA, model quantization, and throughput tuning.

Nice to Have

• Experience with Azure Event Hub, Blob Storage, Application Insights, or similar cloud messaging/storage platforms.
• Familiarity with Docker, cloud deployments, and production monitoring systems.
• Experience in temporal/sequence analysis for event detection.
• Background in video analytics for safety, compliance, or industrial/transportation environments.

Tech Stack

Python, OpenCV, PyTorch, Ultralytics YOLO, ONNX, TensorRT, CUDA, asyncio, aiohttp, gRPC, REST APIs, Azure Event Hub, Azure Blob Storage, Docker, Application Insights (or equivalent telemetry tools).