🛠 Tech Stack

💼 About This Role

You'll join a cutting-edge AI infrastructure team powering the next generation of machine learning workloads. You'll benchmark and optimize GPU platforms for training and inference of large neural networks, working at the intersection of hardware and software. This role offers exposure to modern AI frameworks and international collaboration in a fast-paced, innovation-driven environment.

🎯 What You'll Do

• Benchmark and evaluate GPU platform performance for ML workloads.
• Profile GPU performance and identify optimization opportunities.
• Analyze and debug training and inference workloads for efficiency.
• Develop internal tools and dashboards for performance metrics.

📋 Requirements

• Strong foundation in machine learning and deep learning architectures.
• Deep understanding of performance optimization techniques for neural networks.
• Extensive experience with PyTorch, JAX, Megatron-LM, or TensorRT-LLM.
• Solid expertise with CUDA, NCCL, and GPU driver stacks.

✨ Nice to Have

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

🎁 Benefits & Perks

• 💰 Competitive compensation aligned with experience.
• 🏡 Flexible remote work environment.
• 📚 Continuous learning and career development opportunities.
• 🌍 International collaboration with globally distributed teams.

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

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