Position: Senior ML specialist
Company: Olsys
Experience required: 3y
English level: intermediate

We are looking for Senior ML specialist skilled in CV and NLP (unfortunately we have location restrictions: only EU is available to form a team, recruiting in Ukraine is suspended)

**About the Company**
OLSYS Ltd provides full-service solutions for mid-market and enterprise organizations.

As an enterprise software development company, we are building long term partnerships helping our clients accelerate their digital experiences with reasonable IT investments.
Our tailored approach, e-commerce focus, and flexible solutions allow us to design, develop, and deliver scalable, integrated commerce platforms that drive profits and boost the business.
**15+** years of experience, **100+** projects, **50+** specialists

**About the Client**
As one of the leading and established Process Discovery and Mining Solutions on the market, our client helps businesses transform the way they monitor, analyze and optimize their processes.
Client's company is proud to offer precise instruments for continuous control and improvement providing businesses with significant time and operational cost.

**About the Project**
This is quite a rare opportunity to participate in product development staying in a service company. The product actually provides recommendations on improving the personal working environment on your computer. It analyzes user behavior and provides reasonable recommendations. This project assumes working heavily with text analysis and images processing.

**Domain:** screenshot/document layout understanding, Named entity recognition

**Requirements:**
3+ years professional experience in Machine Learning
Proficiency with machine learning frameworks like **PyTorch**, **PyTorch Lightning**;
Experience in commercial **Computer Vision** (extracting data from images, interpretation) and **NLP** projects
Experience with Transformers, BERT, Object Detection

**English level:** Intermediate +

**Problem formulation: **Detect various entities on screenshot OCR-ed texts. For example, find names, addresses. NER on screenshots is a different task from NER on a raw text.  
There is not usually interconnected text on a screenshot or pdf document. There might be some text fragments located in different parts of a screen, so we cannot just read it left to right, top to bottom. So usual positional encodings are inappropriate here. Instead 2D coordinate information is used.
Another difference is that, besides textual and positional data, we also have visual information here i.e. screenshot image itself. So it can be utilised as a third input source domain. We can use extract information about screenshot layout, text font and other visual clues.
 
**Solution overview:** transformer-based models are utilised for this task. There’re several popular architectures: LayoutLM (v1, v2, v3), DocFormer, Self-Doc. These are all based on using BERT model but differ in how they prepare input data and the way they are pretrained.