🛠 Tech Stack

💼 About This Role

You'll work at the intersection of GPU performance engineering and deep learning optimization, benchmarking advanced GPU platforms for large-scale AI workloads. You'll drive performance improvements across compute architectures and software stacks in a cutting-edge AI infrastructure environment.

🎯 What You'll Do

• Benchmark GPU platform performance for ML/AI workloads across architectures.
• Profile GPU performance and identify optimization opportunities.
• Analyze and optimize training/inference workloads for efficiency and scalability.
• Conduct acceptance testing for new GPU clusters.

📋 Requirements

• Strong foundation in machine learning and deep learning architectures.
• Deep understanding of performance optimization for large neural network training.
• Extensive experience with PyTorch, JAX, or similar deep learning frameworks.
• Expertise with CUDA, NCCL, and GPU software stacks.

✨ Nice to Have

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

🎁 Benefits & Perks

• 💰 Competitive compensation aligned with experience.
• 🏠 Flexible remote work supporting work-life balance.
• 📚 Continuous learning and career development opportunities.
• 🌍 International work environment with globally distributed teams.

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter call· 30 min
2. 2Technical interview· 60 min
3. 3Offer· 30 min