🛠 Tech Stack

💼 About This Role

You'll benchmark and optimize GPU platforms for machine learning and AI workloads in a cutting-edge AI infrastructure environment. You'll drive performance improvements across compute architectures and distributed systems.

🎯 What You'll Do

• Benchmark and evaluate GPU platform performance for ML and AI workloads.
• Collaborate with hardware and engineering teams to profile GPU performance.
• Analyze, debug, and optimize training and inference workloads.
• Conduct acceptance testing for new GPU clusters.
• Develop internal tools and dashboards for performance metrics.

📋 Requirements

• Strong foundation in machine learning and deep learning architectures.
• Deep understanding of performance optimization for neural network training and inference.
• Experience with deep learning frameworks like PyTorch, JAX, or TensorRT-LLM.
• Expertise with GPU technologies including CUDA and NCCL.

✨ Nice to Have

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

🎁 Benefits & Perks

• 💰 Competitive compensation aligned with experience.
• 🏡 Flexible remote work environment.
• 📚 Continuous learning and career development opportunities.
• 🌍 International work environment with globally distributed teams.
• ⚡ High-performance AI systems impact.

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Call· 30 min
2. 2Technical Interview· 60 min
3. 3Systems Design Interview· 60 min