🛠 Tech Stack

💼 About This Role

You'll join a cutting-edge AI infrastructure team, benchmarking and optimizing GPU platforms for large-scale machine learning workloads. You'll drive performance improvements across compute architectures, software stacks, and distributed AI environments. This role offers exposure to modern AI frameworks and high-performance GPU ecosystems with international collaboration.

🎯 What You'll Do

• Benchmark GPU platform performance for ML and AI workloads
• Profile GPU performance and identify optimization opportunities
• Analyze and debug training and inference workloads
• Conduct acceptance testing for new GPU clusters

📋 Requirements

• Deep learning architecture and optimization knowledge
• Experience with PyTorch, JAX, or Megatron-LM
• Expertise in CUDA, NCCL, and GPU software stacks
• Proficiency in Python and performance profiling tools

✨ Nice to Have

• Experience with LLM inference frameworks like vLLM
• Familiarity with cloud ML ecosystems (AWS, GCP, Azure)
• Contributions to open-source ML tooling

🎁 Benefits & Perks

• 💰 Competitive compensation aligned with experience
• 🏠 Flexible remote work supporting work-life balance
• 📚 Continuous learning and career development
• 🌍 International collaboration with global teams

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Final Interview· 45 min