SDE 2 ML Backend Engineer
4 weeks ago
AI Assistant for Research using state-of-the-art language models (ChatGPT for Research)
At SciSpace (Formerly Typeset) , we're using language models to automate and streamline research workflows from start to finish. And the best part? We're already making waves in the industry, with a whopping 4.5 million users on board as of January 2024 Our users love us too, with a 40% MOM retention rate and 10% of them using our app more than once a week We're growing by more than 50% every month, all thanks to our awesome users spreading the word (see it yourself on Twitter). And with almost weekly feature launches since our inception, we're constantly pushing the boundaries of what's possible. Our team of experts in design, front-end, full-stack engineering, and machine learning is already in place, but we're always on the lookout for new talent to help us take things to the next level. Our user base is super engaged and always eager to provide feedback, making Scispace one of the most advanced applications of language models out there.
As a senior software engineer dedicated to developing Gen AI based ML systems, you will be involved in the 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.
ML 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.
Backend Engineering Responsibilities
* Work in managing products as part of SciSpace product suite.
* Partner with product owners in designing software that becomes part of researchers’ lives
* Model real-world scenarios into code that can build the SciSpace platform
* Test code that you write and continuously improve practices at SciSpace
* Arrive at technology decisions after extensive debates with other engineers
* Manage large projects from conceptualization, all the way through deployments
* Evolve an ecosystem of tools and libraries that make it possible for SciSpace to provide reliable, always-on, performant services to our users
* Partner with other engineers in developing an architecture that is resilient to changes in product requirements and usage
* Work on the user-interface side and deliver a snappy, enjoyable experience to your users
Our ideal candidates would
* 5+ 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
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 ML models in production environments using containerization tools like Docker.
* Experience in Distributed systems.
* Experience working with Start-up is a plus point.
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