We are looking for a Junior Machine Learning Engineer who is ready to dive into the exciting field of augmented reality!
If you are a researcher at heart, enjoy solving interesting challenges by using non-standard methods, and want to develop your expertise, we are the right fit for you.
We offer unlimited space for creativity and freedom of action because this is a real research project. Under the mentorship of an experienced leader, you will have the opportunity to test hypotheses, put forward your own ideas, and influence the product. In addition to using modern technologies, you will have the ability to implement and apply the latest Deep Learning articles and techniques directly from arxiv.org.
About the project
Jewelry Ring AR is our own product, which is the best-in-class jewelry try-on application. By using augmented reality, this amazing product enables people to try on jewelry with a mobile phone as if they are in a physical store!
Since there were no ready solutions, frameworks, and neural networks to use in order to get the desired result, we decided to develop a product from the scratch and really succeeded. Our app doesn’t have any competitors both in the way it displays the 3D models of jewelry rings relying on physical-based rendering, as well as in the tryon options that allow moving one’s hand sidewards.
To develop and implement requirements, work on the project involves regular interaction with representatives of the Fashion industry.
- professional development under the guidance of experts
- work in the AR field that represents the future of the industry
- using modern technologies and trying all new creative Deep Learning techniques
- influence on the final product which is the best in its field
Python, TensorFlow, PyTorch, Keras, OpenCV, GCP, Unity, and Blender.
The team consists of 2 ML engineers and a Technical Lead. It is a small, very efficient team in which everyone has the opportunity to make suggestions to boost product and improve the technology stack.
- development of new neural network models, and utilitarian functions for pre- and post-processing;
- implementation of ML algorithms with their further integration on the final device;
- setting up a pipeline for continuous learning;
- analysis and preparation of data for training models;
- running machine learning tests and experiments;
- performing statistical analysis and fine-tuning using test results.
Expertise you need
- 1+ years of commercial experience using Python, TensorFlow, Keras, NumPy, Jupyter, and Sklearn;
- deep knowledge of mathematics: linear algebra, mathematical analysis, statistics, nonlinear optimization;
- deep understanding of machine learning: it is necessary to be familiar with the process of building effective learning systems (data collection, training, evaluation, and improvement);
- candidate should be able to implement neural networks from scratch using no frameworks; (Libraries like NumPy are welcome to be used)
- knowledge of algorithms, and data structures;
- basics of CV algorithms (e.g. Sobel, project 3D to 2D and other transformations (euclidian, similarity, affine);
- the ability to quickly learn something new, understand research work, and reproduce the results of these works;
- English level: Upper-Intermediate.
Nice to have
- experience with TFLite;
- 3D graphics (be familiar with the concept of vertices, faces, and edges of a 3D model);
- GPU computing experience;
- experience in training ML models for work on low-power devices;
- experience in solving CV problems (feature recognition and mapping, PnP, homography conversion, SLAM, and basics of photogrammetry);
- knowledge of C#, Unity, C++, JS;
- experience with augmented reality.
Cozy office or home environment
- MacBook Pro and any extra peripheral devices;
- daily use of modern technologies;
- free lunch at the office;
- flexible working hours;
- ability to work remotely and hybrid;
- paid travel expenses.
Professional development and advancement
- regular 1 on 1 feedback on development and performance;
- unlimited budget for education (books, online courses);
- visit and speak at conferences and technological events worldwide;
- performance-based bonuses;
- technical and career mentorship and guidance.
- experienced and enthusiastic multinational team;
- knowledge sharing culture;
- each employee is responsible for their part of the job so we appreciate the ability to self-organize and prioritize tasks;
- team budget for entertainment;
- referral program – if you want to work even more comfortably, being surrounded by some of your friends, we’ll pay for that up to 1500$ (depending on the candidate level).
- health insurance;
- annual leave of 25 working days;
- paid leave for all important events in your private life;
- financial incentives for the wedding or birth of a child;
- Christmas bonus.
- 30 minutes intro with HR.
That’s a small talk about the company, products, team, position, technology stack, etc.
- 1-hour technical interview with Tech Lead.
In this interview, we will dive deeper into your experience and technical knowledge and have more time to discuss all questions relevant to you.