Advisory Engineer, AI Model Development

Summary:

We are seeking a highly motivated and skilled Model Development Engineer to join our rapidly growing AI team. You will play a critical role in the training of large language models (LLMs), large vision models (LVMs), and large multimodal models (LMMs), including fine-tuning and reinforcement learning. This is a challenging yet rewarding opportunity to contribute to cutting-edge research and development in generative AI. You'll be working with a collaborative team to push the boundaries of what's possible with AI models and deploy them into innovative products. If you are passionate about making Smarter Technology For All, come help us realize our Hybrid AI vision!

Responsibilities:

• Design, implement, and evaluate training pipelines for large generative AI models, encompassing multiple stages of post-training.
  • Design, implement, and evaluate data augmentation pipelines to increase the diversity and robustness of training datasets, improving model performance, particularly in low-data regimes.
  • Develop and implement adversarial training techniques to improve model robustness against adversarial attacks and enhance generalization performance by exposing the model to perturbed input examples during training.
  • Developing and executing SFT strategies for specific tasks.
  • Running and refining RLHF pipelines to align models with human preferences.
  • Design and implement model pruning strategies to reduce model size and computational complexity by removing non-essential parameters, optimizing for both performance and efficiency without significant accuracy loss.
  • Develop and perform model distillation techniques to compress large language models into smaller, more efficient models while preserving key performance characteristics.
  • Implement and evaluate model quantization techniques to reduce model size and accelerate inference speed, balancing precision loss with performance gains for deployment across diverse hardware platforms.
  • Utilizing techniques for efficient fine-tuning of large language models, balancing performance and resource constraints, and tailoring model performance for downstream tasks well.
• Experiment with various training techniques, hyperparameters, and model architectures to optimize performance and efficiency.
• Develop and maintain data pipelines for processing and preparing training data.
• Monitor and analyze model training progress, identify bottlenecks, and propose solutions.
• Stay up-to-date with the latest advancements in large language models, training techniques, and related technologies.
• Collaborate with other engineers and researchers to design, implement, and deploy AI-powered products.
• Contribute to the development of internal tools and infrastructure for model training and evaluation.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field and 5+ years of relevant work experience or 7+ years of relevant work experience.
• Strong programming skills in Python and experience with deep learning frameworks like PyTorch.
• Solid understanding of machine learning principles, including supervised learning, unsupervised learning, and reinforcement learning.
• Proven experience in designing and conducting experiments, analyzing data, and drawing meaningful conclusions.
• Familiarity with large language models, transformer architectures, and related concepts.
• Experience with data processing tools and techniques (e.g., Pandas, NumPy).
• Experience working with Linux systems and/or HPC cluster job scheduling (e.g., Slurm, PBS).

Preferred Qualifications:

• Ph.D. in Computer Science, Machine Learning, or a related field.
• Experience with distributed training frameworks (e.g., DeepSpeed, Megatron-LM).
• Excellent communication, collaboration, and problem-solving skills.