🛠 Tech Stack

💼 About This Role

You'll help scale and optimize training systems and core model code, owning critical infrastructure for large-scale training. You'll work closely with researchers to translate ideas into experiments and production training runs. This is a hands-on, high-leverage role at the intersection of ML, software engineering, and scalable infrastructure.

🎯 What You'll Do

• Own training/inference infrastructure: scheduling, job management, checkpointing, metrics/logging.
• Scale distributed training across TPU and GPU clusters with JAX.
• Profile and optimize memory usage, device utilization, and throughput.
• Build abstractions for launching, monitoring, debugging, and reproducing experiments.

📋 Requirements

• Strong software engineering fundamentals and experience building ML training infrastructure.
• Hands-on large-scale training experience in JAX (preferred) or PyTorch.
• Experience managing training workloads on cloud platforms (e.g., Kubernetes, GCP TPU/GKE, AWS).
• Ability to debug and optimize performance bottlenecks across the training stack.

✨ Nice to Have

• Deep ML systems background (training compilers, runtime optimization, custom kernels).
• Experience operating close to hardware (GPU/TPU performance tuning).
• Background in robotics, multimodal models, or large-scale foundation models.

🎁 Benefits & Perks

• 🏖️ Unlimited PTO
• 💰 Competitive salary and equity
• 🏥 Health, dental, and vision insurance
• 🍱 Daily meals and snacks
• 🚀 Opportunity to work on cutting-edge AI