Software development engineer || ml + core

Sci Space (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 Sci Space cater to various scholarly content creation(B2 C), publication (B2 B publishers), and dissemination (B2 B universities) needs. (Featured in Forbes , Nasdaq , and Entrepreneur magazine.

Sci Space 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 “Sci Space” - the world’s largest social platform for Research - hosting 200 M+ publications and 50 M+ 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 :

3+ years of experience including working on designing multi-component systems

Strong grasp of one high-level language like Python.

General awareness of SQL and database design concepts

Solid understanding of testing fundamentals

Strong communication skills

should have prior experience in managing and executing technology products.

Decent understanding of various Gen AI based ML approaches

Experience in building agentic architectures using langgraph or similar libraries

Bonus :

Prior experience working with high-volume, always-available web-applications

Experience working with cloud

Knowledge of cloud platforms such as AWS, GCP, or Azure.

Experience with deploying small and big open source LLMs in production environments using containerization tools like Docker

Experience in Distributed systems.

Experience working with Start-up is a plus point.

Compensation Range - 25 LPA - 45 LPA