🛠 Tech Stack

💼 About This Role

You'll join the Scaling team to enable production workloads and end-to-end testing on new AI platforms. You'll port inference and training workloads, build benchmarks, and deep-dive into performance bottlenecks for distributed training at scale. This role offers direct impact on the infrastructure behind cutting-edge AI models.

🎯 What You'll Do

• Port and validate inference/training workloads on new platforms
• Build benchmark suites capturing real end-to-end behavior
• Deep-dive performance on distributed training/inference
• Create test harnesses running in CI/lab environments

📋 Requirements

• BS in CS/EE or equivalent practical experience
• 5+ years in ML systems, performance engineering, distributed systems, or HPC
• Strong hands-on experience with PyTorch and modern LLM stacks
• Experience with RDMA and debugging/optimizing comms libraries (NCCL/RCCL)

✨ Nice to Have

• Experience building workload-shaped benchmarks and stress tests
• Familiarity with RDMA networking and transport tuning
• Experience running workloads in Kubernetes

🎁 Benefits & Perks

• 💰 Competitive equity included in total compensation
• 🏖️ Flexible time off
• 🏥 Comprehensive health insurance