Senior ML Engineer

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.