About

Rishabh Shukla is an experienced ML Research Engineer who specializes in leading AI and client delivery teams. He currently holds the position of Lead Machine Learning Engineer at Lifebit, where he supervises the AI Team while also working on the research and development of ML applications for drug discovery and federated multi-omics analysis. With over 8 years of relevant experience, Rishabh has a proven track record of delivering novel AI solutions for multi-omics based immune response prediction and drug discovery to major pharma companies. He is a seasoned professional who has worked with Sales and Business teams, providing initial sales pitch support, technical feasibility analysis, tender filing, and technical support to clients. Prior to joining Lifebit, Rishabh worked at Factmata as an NLP Research Engineer, where he led and supervised a team of 5+ NLP Engineers. He gathered datasets using crowdsourced platforms, expert annotators, distant supervision, and synthetic data generation techniques. He researched and developed ML models for clickbait, toxicity, hate speech, biased content, and stance detection. This included baseline models using hand-curated linguistic features along with variants of SOTA neural models based on contextualized sentence embeddings, Deep RNNs, CNNs, and Transformers architectures. Rishabh also served as the CTO of Neuron, a company providing a distributed NLP platform for vernacular languages. He led a technical team of 10+ Engineers and supervised the development of the company's core NLP Backend and Frontend extension to identify and detect potentially incorrect factual claims on Web pages. Rishabh holds a Bachelor of Technology (B.Tech.) degree in Computer Science from Shri Mata Vaishno Devi University. He has a diverse tech stack that includes ML, AI, NLP, Research Scientist, Web, Senior Software Engineer, Frontend, and Backend. He is a highly motivated individual who is passionate about leveraging his skills and experience to drive innovation and push the boundaries of what is possible in the field of ML and AI.