Senior Machine Learning Scientist, Designer Tools

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Picsart is looking for a Sr. Machine Learning Scientist to join our Picsart AI Research lab and help build, scale, and manage state-of-the-art machine learning algorithms that power Picsart, The ideal candidate will focus on R&D to improve our design tools. Our “striving for excellence” applied research mindset is focused on leveraging the most cutting-edge AI technologies for the constant improvement of our users' experience.

Responsibilities

• Participate in AI lab projects related to fonts generation and editing and other creative tools creation
• Conduct experiments and reproduce results of different papers
• Analyze and improve existing technologies
• Work on quality assessment tools.
• Create and implement non-standard, fresh and creative scientific ideas to improve or speed up existing technologies
• Push the ideas to improve the quality of designed models in terms of product requirements
• Collaborate with product managers and dive deeper into the usage of the research, brainstorm with the product team on developing new creative ideas
• Follow up and support the development of models in an end-to-end manner: from design to production
• Analyze, Interpret and explain outcomes of non-trivial experiments
• Communicate with data and product teams, write clear and understandable research requirement documents
• Collaborate with other scientists, exchange novel and interesting ideas, and reflect these ideas in the research process.

Required Qualifications

• MA/PhD degree in Computer Science or equivalent experience
• Knowledge of Python
• Knowledge of algorithms and data structures
• Knowledge of Deep learning framework: PyTorch/TensorFlow
• Strong math, especially in linear algebra and numerical optimization, probability theory
• Knowledge of SOTA approaches in basic deep learning tasks (classification, segmentation, detection, generation etc.)
• Basic knowledge of classic Machine Learning algorithms
• Experience with style transfer tasks
• Experience with DL models for generative tasks: StyleGan, CLIP, DALL-E etc,.