Senior Gen AI Engineer

About the job

SciSpace (Formerly Typeset) is the world's largest platform for researchers to create, collaborate and publish their research. Used by millions of researchers across 91 countries from organizations such as MIT, Stanford, CERN, NASA, and the like, including Nobel Laureates. Other solutions provided by SciSpace cater to various scholarly content creation(B2C), publication (B2B publishers), and dissemination (B2B universities) needs. (Featured in Forbes, Nasdaq, and Entrepreneur magazine. SciSpace is a series-funded startup. We have raised 4.5 million USD to date, with the most recent fundraise in October 2021 from US-based VC firms. We have expanded our product portfolio with the launch of SciSpace - the worlds largest social platform for Research - hosting 200M+ publications and 50M+ profiles. Think of it like medium.com for hosting and discovering academic papers. We are looking for insatiably curious, always learning Senior Research Scientist. You could get a chance to work on the most important and challenging problems at scale. As a software engineer dedicated to developing Gen AI based ML systems, you will be involved in deployment of ML models, building ML systems and pipelines to ensure reliable systems are deployed at scale to provide value for researchers. This is an engineering dominated role but the candidate should have basic knowledge of ML especially NLP Transformer based (LLM) models to be able to handle the systems better.

Responsibilities

• ML System Development: Design, develop, and maintain scalable and efficient machine learning systems, including writing ML services and APIs.
• Model Deployment: Implement and manage the deployment of machine learning models, including transformer based LLMs, into production environments, ensuring reliability and scalability.
• Infrastructure Management: Collaborate with infrastructure teams to optimize and manage the underlying systems supporting machine learning workflows.
• Data Pipeline Creation: Create robust and efficient data pipelines for collecting, processing, and preparing datasets for machine learning models.
• Collaboration: Work closely with data scientists, researchers, and cross-functional teams to integrate ML solutions into existing software infrastructure.
• Performance Optimization: Continuously optimize and improve the performance of machine learning algorithms and systems.
• Documentation: Develop and maintain documentation for machine learning systems, APIs, and data pipelines to ensure clarity and ease of use for team members.

Our ideal candidates would: