Senior Computer Vision Engineer

Senior AI Engineer — Real-Time Video Systems Location: Uptown Charlotte, North Carolina We are building a high-performance, GPU-accelerated video intelligence platform operating in latency-sensitive, production environments. This role is for a senior engineer who can independently architect, optimize, and scale real-time computer vision systems without supervision. You will own inference performance, model optimization, and production reliability end-to-end. This is not a research-only role. It is not a “train a model and hand it off” role. You will be responsible for making models fast, stable, and production-ready in live environments. If you thrive on squeezing maximum throughput from GPUs, designing resilient inference services, and making real systems perform under load, this will be a strong fit. What You’ll Own • Architect and optimize GPU-accelerated inference pipelines for high-volume video streams. • Drive performance tuning initiatives: batching strategy, frame stride, memory allocation, quantization, and hardware-level optimization. • Implement and refine object detection systems (YOLO-class architectures or equivalent) with temporal filtering and multi-frame logic. • Reduce false positives through tracking, smoothing, and sequence-aware event logic. • Own latency, throughput, and VRAM efficiency metrics — and improve them. • Integrate inference outputs into distributed, event-driven systems and cloud storage layers. • Design production observability: metrics, logging, alerting, and fault-tolerant execution paths. • Collaborate on dataset refinement and model iteration while maintaining a production-first mindset. • Contribute to containerized deployment and scalable runtime infrastructure. What We’re Looking For • 5+ years building and shipping production ML/computer vision systems. • Demonstrated ownership of performance-critical GPU inference pipelines. • Deep proficiency in Python, PyTorch, and OpenCV. • Strong hands-on experience with: • YOLO-class detection frameworks • ONNX and TensorRT optimization • CUDA-level performance tuning • Model quantization and throughput optimization • Solid understanding of video processing fundamentals: • Frame sampling strategies • Temporal filtering and tracking • Confidence calibration • Multi-stream aggregation • Experience deploying containerized workloads (Docker) in production. • Ability to independently diagnose bottlenecks and implement performance improvements without direction. Ideal Profile • You have shipped production systems that operate continuously under load. • You are comfortable profiling GPU memory and compute usage. • You understand the trade-offs between accuracy, latency, and cost. • You prefer building resilient systems over writing academic experiments. • You require minimal oversight and are comfortable defining technical direction within your domain. Core Technology Environment Python, PyTorch, OpenCV, YOLO-class models, ONNX, TensorRT, CUDA, async I/O frameworks, REST/gRPC APIs, event-driven systems, cloud storage/messaging platforms, Docker, production telemetry tools.