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Salary
≈ $62k – $105k per year (Estimated)
Location
Remote (Poland)also open in Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark +24

Confirmed on the employer's own hiring board on Oct 29, 2026. First seen by Alion on Aug 7, 2026. Svitla Systems scores B on the Alion truth index.

Overview
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Impact
Profile match
Svitla Systems is a global digital solutions and custom software engineering company headquartered in California, with delivery centers across North America, Latin America, Europe, and Asia. Founded in 2003, the company offers end-to-end IT services, including cloud architecture, AI/machine learning integration, data engineering, DevOps, and mobile and web application development.

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.
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