🛠 Tech Stack

💼 About This Role

You'll join a cutting-edge AI infrastructure team to benchmark and optimize GPU platforms for large-scale ML and AI workloads. You'll drive performance improvements across GPU architectures, deep learning frameworks, and distributed systems. This role provides exposure to modern AI frameworks and international collaboration.

🎯 What You'll Do

• Benchmark GPU platform performance for ML and AI workloads.
• Collaborate with hardware teams to profile and optimize GPU performance.
• Conduct acceptance testing for new GPU clusters.
• Develop tools and dashboards for performance metrics and trends.

📋 Requirements

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

✨ Nice to Have

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

🎁 Benefits & Perks

• 💰 Competitive compensation
• 🏠 Flexible remote work
• 📚 Continuous learning and development
• 🌍 International work environment
• ⚙️ High-performance AI systems exposure

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

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