🛠 Tech Stack

💼 About This Role

You'll design and optimize large-scale AI pre-training pipelines for advanced language and multi-modal models. You'll conduct experiments and resolve performance bottlenecks to improve model quality and efficiency. This role offers access to cutting-edge GPU infrastructure in a globally distributed research environment.

🎯 What You'll Do

• Design and optimize large-scale AI model pre-training pipelines on distributed GPU systems.
• Research and prototype advanced architectures for LLMs and multi-modal AI.
• Analyze training results and iterate on methodologies to improve model quality.
• Identify and resolve bottlenecks in model performance and computational optimization.

📋 Requirements

• Experience with large language models and transformer architectures.
• Deep understanding of distributed ML systems and GPU training.
• Proficiency in ML frameworks like PyTorch, TensorFlow, or JAX.
• Strong programming skills in Python.

✨ Nice to Have

• Advanced degree in CS, AI, or ML.
• Experience with distributed computing and parallel training strategies.
• Familiarity with data curation strategies.

🎁 Benefits & Perks

• 🌍 Fully remote work environment.
• 🖥️ Access to high-performance GPU clusters.
• 🔬 Collaborate with world-class researchers.
• 📈 Professional growth in AI ecosystems.
• ⏰ Flexible autonomous work culture.

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Call· 30 min
2. 2Technical Screen· 60 min
3. 3On-site Interview· 90 min