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NetEasy LLC

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Senior Software Engineer

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Summary

Senior engineer building a high-load real-time system that runs thousands of parallel instances: reading the screen and processing HD video streams on the GPU while making decisions via algorithmic strategies. Core stack is C++/C# with CUDA/TensorRT/NPP, OpenCV and neural detectors/OCR, plus multithreading, orchestration, and profiling.

Senior Computer Vision / GPU Engineer (C++ / C#): real-time video processing, GPU acceleration, high-load orchestration
We are looking for an engineer for a project where one system runs thousands of instances at the same time. The system reads the screen in real time, processes video streams on the GPU, and makes decisions based on algorithmic strategies.
Please note: this is not web development. The work is on the level of C++/C#, GPU memory, and low-level optimization. We do not consider candidates without real hands-on experience at this level.
Responsibilities
Build and optimize real-time video and image processing pipelines (HD, 30+ fps, dozens of instances per node)
Train and run neural network detectors: object detection, OCR, tracking, pattern matching
Speed up work on the GPU: CUDA, NPP, TensorRT, Video Codec SDK
Multithreading and parallel computing: TBB, process pools, synchronization between instances
Orchestrate and monitor a large number of instances, work with queues and inter-process communication
Profile the system and find bottlenecks: CPU, GPU, I/O
Algorithmic tasks: combinatorial optimization, integer linear programming (OR-Tools / COIN-OR), time series analysis
Requirements
C++ and/or C# on production level, Python for the ML part
OpenCV and neural network detectors (YOLO family, EasyOCR / Tesseract / Nvidia OCR)
Real experience with GPU acceleration: not only calling a ready framework, but also profiling, memory management, and batch strategies
PostgreSQL, Docker, message brokers, gRPC or REST
English from Intermediate (B1)
Nice to have
Projective geometry: homography, perspective correction, camera calibration
Full MLOps cycle: data labeling → training → deployment
Algo trading: ARIMA, regression analysis, RANSAC
Experience with systems that handle thousands of parallel sessions

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