Cornerstone provides an AI-powered Talent Experience Platform for unified content discovery, knowledge management, and personalized learning platform for your career journey. Our award-winning Platform is used globally by Fortune 500 companies and government organizations to solve content discovery, curation, and recommendation problems across external, internal, and tacit knowledge sources

In the Data Science and Machine Leaning team at Cornerstone we are looking for solid hands-on technologists with solid time-management skills and experience in highly autonomous roles. You also function effectively in a collaborative environment and are comfortable making independent decisions.

Key Result Areas include:
Lead and drive execution of new initiatives for Machine Learning using conversational NLP techniques and introduce ML-based learning for recommendation and coaching assistant in our products
Lead team of ML engineers in evaluating ML technologies, building/training models, deployment of models and monitoring in production environments.
Work with the engineering teams and ensure timely deliveries.
Be part of a global Engineering team supporting Fortune 1000 customers worldwide.
Ability to experiment and iterate rapidly and provide tangible improvements in the overall engagement

You Are:
A driven team player, collaborator, and relationship builder whose infectious can-do attitude inspires others and encourages excellent performance in a fast-moving environment.
Results orientated, motivated by success.
Self-motivated: You can work with minimal supervision and be able to strategically prioritize multiple tasks proactively.


What you need:
Master's or bachelor's degree in computer science or a related study or equivalent experience.
7+ years of hands-on experience architecting and designing highly scalable and resilient systems.
5+ years of experience in designing & scaling applications based on Data Science, NLP, and conversational AI
Understanding of ML algorithms - classical and deep learning and ML frameworks
Good understanding of system availability, security, and performance management.

In particular, the candidate should have work experience with:


PyTorch and its ecosystem of libraries
Word and document embeddings
Transformers and Attention
RNN, LSTM
A background in BERT and its variants
transfer-learning practices
NLP Libraries: NLTK, Genism , Spacy
ML-pipelines – Apache Airflow/ Kubeflow/ RAY
OpenAI libraries and Models - LLMs

Besides this, the candidate must possess a mature understanding of, and hands-on experience in, the broad field of machine-learning and statistical methods. Must be conversant with:


scikit-learn
Pandas
Numpy
plotting using matplotlib, seaborn, etc.
HuggingFace

Beside the knowledge of framework or libraries, having experience in building the recommendation systems with following algorithms will be considered as a plus:
Generative Models i.e. VAE & GAN
Collaborative based filtering/ Context aware/Graph based recommender systems
Factorization Machine
Deep recommender



Experience with any of the following is considered a plus:
Tensorflow
Big-data and PySpark
Data wrangling libraries: Beautiful Soup or Scrapy
R libraries
Taking models to production
Cloud Technologies: GCP/AWS
ML Graph Models GNN and NetworkX