AI Scientist - Product Engineering

AI Scientist – Product Engineering

Master’s degree in Computer Science, Engineering, or related, or a minimum of 2 years hands-on experience post undergraduate degree (BE, BS, or equivalent)
Minimum of 1 year industry experience in Artificial Intelligence (AI), Natural Language Processing (NLP), LLMs, deep learning, Computer Vision, or Automatic Speech Recognition (ASR)
Understands metrics, ML applications, and data science (DS) concepts; curious and inquisitive to explore variations and innovations
High level programming skills (Python) and good understanding of ML frameworks and tools such as PyTorch lightning, Kubeflow, TensorFlow and Transformers
Skilled with algorithms, data structures, and object-oriented programming
Experience with cloud tech stack (AWS or GCP) and ability to develop ML models in a cloud environment
Knowledge of deployment using environments such as: Docker, and Kubernetes
Experience in Python programming and developing production-ready, scalable code; able to use SQL, and S3 datastores
Good oral and written communication abilities, including ability to present one’s own work to others

Evaluate, adapt, and develop new state of the art language and/or multimodal foundation models for enterprise applications
Perform thorough empirical analysis of the approach and write production ready code
Develop, modify and fine-tune ML/NLP and pre-trained enterprise-grade large language models (LLMs) and multi-modal models
Ensure the developed models seamlessly integrate with other tools and company products and are aligned with the feedback that will be generated from enterprise customers
Specify guidelines and design annotation processes for data; collaborate with data team on data preprocessing, consumption, and automated/manual annotations
Evaluate product and algorithm performance using industry standard approaches and develop new evaluation paradigms; build up competencies comparing results of more complex approaches
Collaborate with other teams, provide input, and contribute to the delivery of AI services for critical projects
Write concise, readable, and modular code, continually improving the code, requiring minimal revision when moved to production
Manage large datasets, address and resolve quality issues, and interpret data with a focus on business goals
Develop clear documentation of the work plan, its evolution, and results
Regularly read and assess publications on existing methods, and compare internal solutions to alternative method

No preferred qualifications provided.