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C++ ENGINEER WITH COMPUTER VISION

Svitla Systems · Remote - Poland · 2026-08-07

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Job description

Svitla Systems Inc. is looking for a Senior C++ Engineer with Computer Vision for a full-time position (40 hours per week) in Poland. Our client is an American mobile computing company.

You will bridge the gap between high-level machine learning research and high-performance production environments. Also, you will be responsible for building robust SDKs, automating deployment pipelines, and ensuring the models run efficiently across a diverse hardware landscape, from edge SoCs to powerful cloud GPUs.

Requirements

- Bachelor's or master's degree in Computer Science, Electronic Engineering, or related technical field.

- Deep understanding of C++14/17/20, including STL, memory management, and multi-threading.

- Expertise in writing clean, maintainable Python for automation and data processing.

- Hands-on experience with at least one of the following frameworks:

- SNPE/QNN (Qualcomm);

- OpenVino (Intel);

- TensorRT (NVIDIA);

- TensorFlow Lite.

- Familiarity with Docker for creating consistent development and deployment environments.

Nice to have

- Understanding of deep learning fundamentals (CNNs, Transformers, Object Detection).

- Knowledge of model conversion and quantization (i.e., PTQ, QAT).

Responsibilities

- SDK Development (C++): Design, develop, and maintain high-performance software development kits (SDKs) to expose computer vision capabilities to end users and internal products.

- Model Deployment and Integration: Port, convert, and deploy machine learning models across various hardware targets, including Qualcomm SoCs, Intel CPUs, and NVIDIA GPUs.

- Performance Optimization: Use hardware-specific toolkits to optimize model throughput without sacrificing accuracy.

- Evaluation & Benchmarking: Conduct rigorous testing and evaluation of models on target hardware to ensure performance metrics meet expectations.

- Automation: Build and maintain automation scripts and CI/CD pipelines in Python to streamline the model testing and deployment lifecycle.