Research Scientist (RS-I) - Recommendation Systems/NLP (PhD degree holders ONLY)

NOTE: CTC bracket negotiable for exceptional candidates*

About CRIMSON AI

We are building a unique research platform and products that partners with researchers at every stage of their research and help them focus on the science and leave the rest to our AI powered solutions. Our products use cutting-edge AI technologies to deliver the best results and experience to researchers across the world.

About the team

We are a team of passionate researchers, data scientists, engineers, language lovers, linguists and designers building good automation and AI software for the scholarly publishing industry. Our AI-powered products are driven by natural language processing and machine learning to help both authors and publishers in their publication goals. Our products ease the human burden at every stage of the publication cycle, from manuscript writing to knowledge dissemination.

We believe research plays a key role in making the world a better place, and we want to make it easy to approach and fun to do!

Role Overview

We are actively looking for ambitious and brilliant Research Scientists who would be excited to solve some of the most challenging problems in the field of Scholarly Document Processing, Scholarly Document Recommendation, & Scholarly Document Knowledge Discovery and Management. Please have a look at our flagship products i.e.

1. https://www.trinka.ai/
2. https://www.raxter.io/
3. https://www.authorone.ai/

We are forming a formidable research team with active external collaborations from some of the leading academic minds in the field. Our research culture focuses on a perfect balance between product-oriented research leading to cutting-edge proprietary IP, and exploratory research leading to top-notch publications and opensource frameworks. To have an understanding of the sort of NLP-ML/Recommendation research that's currently going on, have a peep at raxter.io

As a Research Scientist - I (full-time) you should have 4+ years of research experience (including your Ph.D. program) with a very strong foundation in either: (a) classical NLP and neural NLP, or (b) scalable personalized and adaptive recommendation systems designing with a focus on explainability and serendipity, or (c) Knowledge Graphs, Ontologies, Relation Learning, and Reasoning.

You must also have demonstrated your research ability in the field of (or in closely related fields) Scholarly Document Processing, Scholarly Document Recommendation, & Scholarly Document Knowledge Discovery and Management through relevant tier-1 conference (CORE ranking A*, A) and journal (Quartile-1) publications.

Please note that Ph.D. degree holders (also eligible are those who are about to defend their thesis) from top Indian universities (eg: IIT-K, IIT-KGP, IIT-B, IIT-M, IIT-G, IIT-D, IISc, IIIT-H, IIIT-D, ISI, etc.) will be preferred. Indians coming back from top-class international universities are also highly preferred.

The person that we are looking for

We are looking forward to seeing within you a great personality (someone who can equally share our dream of making a more creative and innovative generation and who is a great person to hang around with). You should have the strong desire to be a part and parcel of the RAx movement (please do research on us for that) for the long-term with ownership.

Responsibilities

You are expected to show excellent performance in the following tasks:

1. Working on challenging problems (NLP-ML-KG-Recommendation) related to scholarly document processing
2. Updating yourself with all relevant recent and past research works
3. Publications at A+/A level venues
4. Optimized algorithm and code design & execution
5. High-quality Test-driven-development
6. Deadline estimation
7. Benchmarking
8. Research team building

Experience

1. Demonstrated solid research skills through publications at A+/A- journals/conferences in any one of the fields: i.) Recommendation Systems (with strong knowledge in reinforcement learning based techniques) ii.) Information Extraction & Mining (preference for scholarly document mining) iii.) Knowledge Graph Representation & Learning
2. Extremely strong in (and passionate about) mathematics & statistics (specifically, but not limited to, linear algebra, probability theory, regression modeling, Bayesian statistics, mathematical logic, linear and non-linear optimization, learning theory)
3. Multi-paradigmatic approach for solving AI problems
4. Love for publication in top venues.
5. Very strong skill in Python.
6. Strong skill in Cython/C/C++.
7. Very strong in data structures and algorithms.
8. Good understanding of RESTful API and micro-server architecture.
9. Very good understanding of DBMS and search engine frameworks (eg: Elasticsearch).