🛠 Tech Stack

💼 About This Role

You'll design and optimize large-scale AI model pre-training pipelines on distributed GPU infrastructure, working with world-class researchers on advanced language and multi-modal models. Your core impact will be improving model intelligence, efficiency, and scalability through experimentation. This role stands out with cutting-edge infrastructure powered by thousands of GPUs.

🎯 What You'll Do

• Design and optimize large-scale AI model pre-training pipelines.
• Research and prototype advanced architectures for LLMs and multi-modal AI.
• Conduct experiments, analyze results, and iterate on training methodologies.
• Identify and resolve bottlenecks in model performance and scalability.

📋 Requirements

• Strong experience with large language models and transformer architectures.
• Deep understanding of distributed ML systems and large-scale GPU training.
• Proven expertise in PyTorch, TensorFlow, JAX, or similar frameworks.
• Strong programming in Python and experience building scalable AI training systems.

✨ Nice to Have

• Advanced degree in Computer Science, AI, or related field.
• Familiarity with distributed computing and parallel training strategies.

🎁 Benefits & Perks

• 🏖️ Fully remote work environment.
• 💻 Cutting-edge AI research and large-scale pre-training systems.
• 🚀 Advanced GPU clusters and computing infrastructure.
• 🌍 Collaborative global culture with world-class talent.
• 📈 Professional growth in rapidly evolving AI ecosystems.

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Research Deep Dive· 60 min