Machine Learning Engineer, Artificial Intelligence Pioneer

Qualifications

• BS/MS in a quantitative field (Computer Science, Maths, Statistics, Physics, etc.) and 4+ years of experience
• Proficient programming skills in Python
• Hands-on experience working with deep learning toolkits such as Jax, Tensorflow, or PyTorch
• Proven track record in training or deployment of large models or building large-scale distributed systems
• Deep understanding of Deep Learning and Large Language Models (LLMs)
• Natural Language Processing

Responsibilities

• Develop and deploy large-scale machine learning models for personalization
• Work on core machine learning algorithms and models to power personalization
• Collaborate with cross-functional teams to drive innovation and efficiency

Full Description

Personalization Revolution

Are you passionate about building extraordinary products and embracing state-of-the-art techniques? It's central to all that we do. The goal of our team is to take personalization, search, and more to the next level of intelligence and accuracy using advanced machine learning and statistical techniques.

We work on a wide spectrum of approaches, including foundation modeling, deep learning, model compression, transfer learning, federated learning, and more. We develop core machine learning algorithms and models to power personalization in an efficient and scalable manner. This position involves a wide variety of skills and innovation.

Key Responsibilities

• Develop and deploy large-scale machine learning models for personalization.

• Work on core machine learning algorithms and models to power personalization.

• Collaborate with cross-functional teams to drive innovation and efficiency.

Requirements

• BS/MS in a quantitative field (Computer Science, Maths, Statistics, Physics, etc.) and 4+ years of experience.

• Proficient programming skills in Python.

• Hands-on experience working with deep learning toolkits such as Jax, Tensorflow, or PyTorch.

• Proven track record in training or deployment of large models or building large-scale distributed systems.

• Deep understanding of Deep Learning and Large Language Models (LLMs).

• Natural Language Processing.