Deep Learning Researcher (вакансія неактивна)

— 2+ years of practice in the Machine Learning / Deep Learning field.
— Strong Python knowledge.
— Hands-on experience with AI frameworks (such as Tensorflow, PyTorch, etc).
— Excellent interpersonal skills, cross-group, and cross-culture collaboration.
— Proven ability to develop original ideas.
— At least Upper-Intermediate English level.

— Understanding of RL techniques.
— Experience with OpenAI Gym.

— Startup culture, join a dynamic fast-moving team with high energy and an ambitious proactive can-do approach.
— Working trips to Israel.
— Be part of a wider team and create an impact in a fun environment that is going through an exciting time of massive growth and innovation.
— Office in Kyiv downtown.
— Opportunity to be part of the big story that shapes the future and is already changing the present.
— Flexible working hours.

— Develop algorithms around some key research areas in Machine Learning. This requires constant paper reading and staying ahead of the game by knowing what the state of the art is now and will be in the future in this exciting field.
— Learn the newest Reinforcement Learning and NLP techniques.
— Reach quick Proof of Concept (POC) features ownership around Deep Learning, mixed with traditional approaches. POCs typically result in rich experience for the organization and help us evaluate the feasibility of projects.
— Improve your skills and stay up-to-date with emerging technologies.

DataRoot Labs is looking for a Deep Learning Researcher! We need a person who would share our passion for driving the Deep Learning market forward and putting Ukraine on the map of global industry leaders. We solve challenging problems and are continuously leveraging and advancing the latest AI research in deep and reinforcement learning.

On this project, you will be working in a fast-changing entrepreneurial environment with a team of young and ambitious professionals. You will be a part of the team which works on the Empathetic Dialogue System, where we apply the latest advances in NLP, Reinforcement Learning, Voice Modeling, and Generative Modeling. Almost all the NLP tasks are solved on top of language models such as GPT-2 or BERT. Reinforcement learning is used to learn on real user-system interactions and orchestrate the utterance generation conditions and the next word probabilities tweaking.