AIML - ML Research Engineer, Forecasting Foundation Models
Job Description
Summary
We are a group of innovative researchers and engineers dedicated to fundamentally increase Apple’s business success across Finance, Sales, and Operations with Deep Learning technologies and innovations. We build foundation models to forecast critical business and financial metrics that drive optimization for top line as well as bottom line growth!
Are you a Research Engineer who is passionate about building algorithms and systems to advance Multi-horizon Demand Forecasting, Generative Sales Strategies, Generative Supply Planning, and Holistic Optimization to the next level? As part of the team, you'll play a pivotal role in redefining Apple’s product lifecycle and business decision-making through novel Deep Learning research pursuits. This role presents outstanding opportunities to innovate in Forecasting Foundation Models, Multi-modal Learning, Sequential Recommendation Learning, Explainable ML and Reinforcement Learning. Through working with extraordinary domain experts from Finance, Sales, Operations, and Operations Research engineers you'll make significant impacts on Apple's core businesses.
Are you a Research Engineer who is passionate about building algorithms and systems to advance Multi-horizon Demand Forecasting, Generative Sales Strategies, Generative Supply Planning, and Holistic Optimization to the next level? As part of the team, you'll play a pivotal role in redefining Apple’s product lifecycle and business decision-making through novel Deep Learning research pursuits. This role presents outstanding opportunities to innovate in Forecasting Foundation Models, Multi-modal Learning, Sequential Recommendation Learning, Explainable ML and Reinforcement Learning. Through working with extraordinary domain experts from Finance, Sales, Operations, and Operations Research engineers you'll make significant impacts on Apple's core businesses.
Description
Your contributions will have extensive and direct monetary impact on Apple by transplanting groundbreaking deep learning models into explainable and tangible business solutions. You will closely work with outstanding engineers and researchers to solve some of the most ambitious problems: AI-Generated Financial Optionality, Foundation Models for demand forecasting, Reinforcement Learning with knowledge distillation, and Explainable Deep Learning.
Minimum Qualifications
- Demonstrated expertise in deep learning with a proven publication record in reputable conferences like NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, KDD, ACL, ICASSP, InterSpeech. Or a track record applying deep learning techniques to real-world problems
- Proficient programming skills in Python and hands-on experience with at least one of the toolkits for Deep Learning, such as JAX, PyTorch, or Tensorflow
- In-depth experience in relevant areas such as Quantitative Trading, Foundation Models (e.g. LLMs), Sequential Representation Learning, and Generative AI
- Sound business intuitions, adaptive mentality and the courage to change established business processes
- 5+ years of industrial experience in the Deep Learning or Quantitative Research space
Preferred Qualifications
- PhD or equivalent experience in Computer Science, Machine Learning, Mathematics is helpful
