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.

Originally posted on Himalayas