Senior ML Engineer

Anastasiia Durnieva

Recruiter

a.durnieva@geniusee.com

+380 95 899 34 46

@stasy2000

Apply 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.

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.

Please be informed that the data administrator is Geniusee LLC with headquarter at BC Y4, Yaroslavsky Lane 4, 04071, Kyiv, Ukraine. Processing of personal data is carried out in accordance with the Law of Ukraine "On Protection of Personal Data" dated 01.06.2010 No. 2297-VI. You have the right to request access to your personal data, their release, removal or restriction of admission, the right to make a warning against admission, as well as the right to transfer the data and to make arrangements. The submission of data is voluntary and this personal data is processed in order to manage candidate applications and recruitment (selection process, interview follow-up, your job alerts management). Refusal to provide these obligations may result in a lack of opportunity to conduct the recruitment process.

Candidate personal data is addressed to Geniusee and is available to our recruitment teams, our employees involved in the recruitment process, our HR teams, and our IT teams which administrate our tools. As a part of our recruitment process evaluation, some of your data (name, surname, email address) may be used in charge of organizing events during which we may invite certain candidates.