Machine Learning Engineer at Pond

Job Description

Key Responsibilities

• Design and implement large-scale model training using distributed deep learning frameworks such as PyTorch, TensorFlow, Ray, etc.
• Manage and optimize model training and inference processes to ensure high performance and efficiency.
• Containerize deep learning applications using Docker and orchestrate them using Kubernetes and Kubeflow.
• Deploy and manage deep learning workloads on major cloud platforms including AWS, Google Cloud, and Azure.
• Apply model compression and inference acceleration techniques to optimize performance.
• Implement stream batch data inference techniques for real-time processing.
• Collaborate with cross-functional teams to develop and execute technical strategies for distributed computing and deep learning solutions.

Job Requirements

• Extensive experience in deep learning frameworks (PyTorch, TensorFlow, etc.) and model training/optimization.
• Strong expertise in containerization (Docker) and orchestration techniques (Kubernetes, Kubeflow).
• Proven experience with cloud computing platforms (AWS, Google Cloud, Azure).
• Preferred experience in CUDA programming and multi-GPU communication optimization.
• Knowledge of stream batch data processing techniques.
• Ability to work collaboratively in a team environment and contribute to technical strategy development.
• Strong problem-solving skills and ability to work on cutting-edge AI/ML projects.

Preferred Qualifications

• Experience with Ray or other distributed computing frameworks.
• Background in decentralized AI/ML systems.
• Publications or contributions to open-source projects in relevant fields.