SciSpace - Senior AI Research Scientist

Looking for someone with over 5+ years of experience in AI Research and Engineering.

SciSpace is a product-based startup.

AI Assistant for Research using state-of-the-art language models (ChatGPT for Research). At SciSpace, 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 5 million users on board as of November 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.

AI Research Scientist Responsibilities :

- Being able to fine-tune models, build your own models from scratch.

- Being able to read and understand new research, and deploy them into a suitable testing environment.

- Ability to create, evaluate and deploy testing and evaluation frameworks specific to different models.

- Ability to work on various agentic frameworks and build PoCs around it.

- Proven experience with large-scale LLMs and Deep Learning systems.

- Strong programming skills; versatility is a plus.

- Self-starter with a willingness to take ownership of tasks.

- Passion for tackling challenging problems.

- Minimum of 2 years of working on relevant projects.

- 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 :

- 5+ years of experience including working on designing multi-component ML-based systems.

- Strong NLP experience working on various research-oriented projects.

- Very strong understanding of various Gen-AI based ML approaches.

- Strong experience working on various ML frameworks like pytorch, tensorflow, huggingface, etc.

- 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.

Bonus :

- Any research publication in the ML domain is a big bonus.

- Experience on agentic frameworks like autogen, langgraph, crewai, etc.

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

- Experience working with the 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-ups is a plus point.