UST, formerly known as UST Global, is a provider of digital technology and transformation, information technology, and services, headquartered in Aliso Viejo, California, United States. Stephen Ross founded UST in 1998 in Laguna Hills. The company has offices in the Americas, EMEA, APAC, and India.
You will be working closely with the business, including stakeholders and essential partners internally at Dell Technologies to design, build and productionize end-to-end machine learning solutions & products for our business and customers.
Responsilibities:
Design & build scalable, efficient & automated processes for large-scale data analysis, machine-learning model development, model validation and servings.
Conduct experimentation to improve model performance.
Participate in brainstorming sessions.
Ability to effectively communicate, both verbally and in writing to scientists and non-scientists.
Works independently with internal and external teams.
8+ years of experience in AI, ML, DL, and Reinforcement Learning (RL), with an emphasis on Research in AI/ML/DL technologies and a MS/PhD/Bachelors’ degree or a combination of experience and education.
Extensive knowledge and practical experience with deep learning, reinforcement learning, and Graph Neural Networks.
Solid understanding of AI model architecture, training paradigms, fine-tuning processes, and optimization techniques (such as Gradient Descent, Adam, RMSProp).
Innovate through advanced research and development in the fields of Large Language Models (LLMs), Generative AI, Deep Learning, implementing and fine-tuning architectures using frameworks like PyTorch and TensorFlow to tackle complex ML/DL problems.
Employ techniques like pruning, quantization, and model compression for optimizing AI model performance and efficiency, ensuring robustness and scalability of solutions.
Document intricate research algorithms and models succinctly, contributing to technical reports, code annotations, and ensuring a comprehensive repository of research findings.
Software engineering experience particularly related to productionizing ML models and scaling them in low-latency settings.
Proficient in Data Mining, Data transformation and Database building ( ETL, SQL OLAP, Teradata, Hadoop ).
Knowledge of cloud-native computing DevOps, data streaming and extraction, parallelized workloads using Docker, Kubernetes, Test Driven.
Essential Requirements:
A Ph.D with 4 years work experience or Master's degree with 6 years’ work experience in Computer Science, Machine Learning, Natural Language Processing, or a related field.
Proven hands-on experience in NLU/NLP projects, with a track record of successful implementations with proficiency in programming languages such as Python, and experience with popular NLP libraries and frameworks (e.g., NLTK, SpaCy, TensorFlow, PyTorch).
Strong understanding of statistical and machine learning techniques used in NLP, including word embeddings, deep learning architectures, and sequence-to-sequence models.
Excellent analytical and problem-solving skills, with the ability to analyze large-scale datasets and derive meaningful insights.
Experience with Large Language Models (LLMs) such as GPT-3, GPT-4, or similar, and their applications in various NLP tasks.
Proven experience with recommender systems, including applied research and experience with context aware machine learning frameworks 5+ years.
Proven experience with deep learning frameworks such as Tensorflow, Tensorflow Recommenders, Pytorch, MXNet, Keras.
Rock star PostgreSQL & Python.
Demonstrate advanced foundation in Linear Algebra, Calculus and Statistics.
Experience in applying both sequence based and traditional methods to recommender problems – e.g. convolutional neural nets, recurrent neural nets, LSTMs, restricted Boltzmann machines, adversarial networks, collaborative filters, XGBoost, classical clustering techniques.
Machine learning, Deep Learning, NLP, Python, Machine learning Algorithms [Primary skills], AI, ML, DL, and Reinforcement Learning (RL), with an emphasis on Research in AI/ML/DL technologies and a MS/PhD/Bachelors’ degree or a combination of experience and education, PyTorch and TensorFlow to tackle complex ML/DL problems, Large Language Models (LLMs), deep learning frameworks such as Tensorflow, Tensorflow Recommenders, Pytorch, MXNet, Keras, PostgreSQL & Python.