About the project:
An AI platform that upgrades legacy CCTV with real-time detection, instant incident notifications, and decision-ready analyticsdesigned with GDPR and responsible AI at the core.

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.

What you will get.
Competitive salary and good compensation package;
Exciting, challenging and stable startup projects with a modern stack;
Corporate English course;
Ability to practice English and communication skills through permanent interaction with clients from all over the world;
Professional study compensation, online courses and certifications;
Career development opportunity, semi-annual and annual salary review process;
Necessary equipment to perform work tasks;
VIP medical insurance or sports coverage;
Informal and friendly atmosphere;
The ability to focus on your work: a lack of bureaucracy and micromanagement;
Flexible working hours (start your day between 8:00 and 11:30);
Team buildings, corporate events;
Paid vacation (18 working days) and sick leaves;
Cozy offices in 2 cities ( Kyiv & Lviv ) with electricity and Wi-Fi (Generator & Starlink);
Compensation for coworking (except for employees from Kyiv and Lviv);
Corporate lunch + soft skills clubs;
Unlimited work from home from anywhere in the world (remote);
Geniusee has its own charity fund.