AI Engineer- Video Analytics

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    "description": "<p style=\"margin-bottom:11px;\"><span style=\"font-size:11pt;\"><span style=\"line-height:107%;\"><span style=\"font-family:Calibri, sans-serif;\"><b><span style=\"font-size:10pt;\"><span style=\"line-height:107%;\">AI Engineer: Video Analytics</span></span></b></span></span></span></p>\r\n\r\n<p style=\"margin-bottom:11px;\"><br />\r\n<span style=\"font-size:11pt;\"><span style=\"line-height:107%;\"><span style=\"font-family:Calibri, sans-serif;\">Location: Charlotte, NC<br />\r\nEmployment Type: Full time</span></span></span></p>\r\n\r\n<p style=\"margin-bottom:11px;\"><span style=\"font-size:11pt;\"><span style=\"line-height:107%;\"><span style=\"font-family:Calibri, sans-serif;\">The VTrack 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.</span></span></span></p>\r\n\r\n<p style=\"margin-bottom:11px;\"><span style=\"font-size:11pt;\"><span style=\"line-height:107%;\"><span style=\"font-family:Calibri, sans-serif;\">If you enjoy pushing GPU performance limits, crafting resilient ML pipelines, and building real world safety applications that make an impact, you&rsquo;ll fit right in.</span></span></span></p>\r\n\r\n<div align=\"center\" style=\"text-align:center;margin-bottom:11px;\">\r\n<hr align=\"center\" size=\"0\" width=\"100%\" /></div>\r\n\r\n<p style=\"margin-bottom:11px;\"><span style=\"font-size:11pt;\"><span style=\"line-height:107%;\"><span style=\"font-family:Calibri, sans-serif;\">Responsibilities</span></span></span></p>\r\n\r\n<ul style=\"margin-bottom:11px;\">\r\n\t<li style=\"margin-bottom:11px;\"><span style=\"font-size:11pt;\"><span><span style=\"line-height:107%;\"><span style=\"font-family:Calibri, sans-serif;\">Develop and optimize GPU accelerated video inference pipelines, including batching, stride control, and throughput tuning.</span></span></span></span></li>\r\n\t<li style=\"margin-bottom:11px;\"><span style=\"font-size:11pt;\"><span><span style=\"line-height:107%;\"><span style=\"font-family:Calibri, sans-serif;\">Implement, evaluate, and improve object detection models (YOLO or similar) and build temporal smoothing/tracking logic for safety event detection.</span></span></span></span></li>\r\n\t<li style=\"margin-bottom:11px;\"><span style=\"font-size:11pt;\"><span><span style=\"line-height:107%;\"><span style=\"font-family:Calibri, sans-serif;\">Optimize model performance using TensorRT, ONNX, CUDA, and GPU profiling tools to maximize throughput and minimize latency/VRAM usage.</span></span></span></span></li>\r\n\t<li style=\"margin-bottom:11px;\"><span style=\"font-size:11pt;\"><span><span style=\"line-height:107%;\"><span style=\"font-family:Calibri, sans-serif;\">Build and maintain integrations with event-driven APIs, Azure Event Hub, Blob Storage, and internal services.</span></span></span></span></li>\r\n\t<li style=\"margin-bottom:11px;\"><span style=\"font-size:11pt;\"><span><span style=\"line-height:107%;\"><span style=\"font-family:Calibri, sans-serif;\">Add robust metrics, logging, telemetry, and fail safe mechanisms for resilient inference jobs.</span></span></span></span></li>\r\n\t<li style=\"margin-bottom:11px;\"><span style=\"font-size:11pt;\"><span><span style=\"line-height:107%;\"><span style=\"font-family:Calibri, sans-serif;\">Collaborate on dataset curation, labeling, model training, validation, and experiment tracking.</span></span></span></span></li>\r\n\t<li style=\"margin-bottom:11px;\"><span style=\"font-size:11pt;\"><span><span style=\"line-height:107%;\"><span style=\"font-family:Calibri, sans-serif;\">Support containerized deployments (Docker) and assist with monitoring and scaling production workloads.</span></span></span></span></li>\r\n</ul>\r\n",
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