Вакансія Senior ML Engineer

Skills

• Python
• C++
• TensorRT
• ONNX
• TFLite
• OpenVino
• CoreML
• NVIDIA Jetson Nano
• Qualcomm Snapdragon
• Raspberry Pi

Опис вакансії

Our mission at Geniusee is to help businesses thrive through tech partnership and strengthen the engineering community by sharing knowledge and creating opportunities. Our values are Continuous Growth, Team Synergy, Taking Responsibility, Conscious Openness and Result Driven. We offer a safe, inclusive and productive environment for all team members, and we’re always open to feedback. If you want to work from home or work in the city center of Kyiv, great – apply right now.

Requirements

• 4+ years of hands-on experience in computer vision, with a strong focus on embedded/edge device optimization;
• Deep knowledge of CNN-based object detection and face detection techniques;
• Expertise in model compression, quantization, and acceleration for embedded inference;
• Strong Python and C++ development skills;
• Experience with deployment tools: TensorRT, ONNX Runtime, TFLite, OpenVINO, or CoreML;
• Familiarity with embedded platforms (e.g., NVIDIA Jetson, Qualcomm Snapdragon, Raspberry Pi with Coral Edge TPU);
• Experience in profiling and optimizing models for latency, throughput, and memory efficiency.

What you will do

• Communication: Lead as part of a scrum team focused on delivering production-ready, optimized computer vision models. Promote clear, respectful, and constructive communication;
• Collaboration: Work closely with machine learning engineers, software developers, and hardware teams to ensure seamless integration of vision models into products;
• Documentation: Maintain high standards for documenting model training processes, optimization techniques, deployment pipelines, and performance benchmarks;
• Continuous improvement: Stay updated on state-of-the-art research in embedded computer vision and edge AI, bringing new ideas and technologies into the development process;
• Planning: Actively participate in sprint planning and provide accurate estimations, particularly for model optimization and deployment tasks.
• Model Development and Optimization:
  • Design and optimize CNN-based object detection and face detection models;
  • Implement advanced model optimization techniques, including pruning, quantization, and knowledge distillation;
  • Develop models with a focus on efficient deployment on embedded or edge devices.
• Embedded Deployment:
  • Use tools like TensorRT, ONNX Runtime, TFLite, OpenVINO, or CoreML for deployment;
  • Work with embedded hardware platforms such as NVIDIA Jetson, Qualcomm Snapdragon, and Raspberry Pi with Coral Edge TPU.
• Performance Profiling:
  • Profile models for latency, throughput, and memory efficiency;
  • Optimize the inference performance to meet strict resource and timing constraints on target devices.
• Development Skills:
  • Develop and maintain codebases in Python and C++ to support model integration and deployment pipelines.