🛠 Tech Stack

💼 About This Role

You'll join a high-impact AI Platform group building foundational systems for large-scale model training. You'll design and evolve infrastructure that enables distributed AI training workloads to run reliably and efficiently at scale. Your work will directly support research scientists and ML engineers in deploying advanced AI capabilities.

🎯 What You'll Do

• Design and scale core training platform infrastructure for distributed AI workloads
• Improve reliability, observability, and debugging of large-scale training systems
• Enhance scheduling, resource allocation, and quota management for AI training jobs
• Collaborate with ML engineers and researchers to optimize training workflows

📋 Requirements

• Strong experience in machine learning infrastructure or distributed systems
• Hands-on expertise with Kubernetes and containerized environments
• Familiarity with distributed training frameworks like Ray or PyTorch distributed training
• Experience working with cloud infrastructure for high-performance workloads

✨ Nice to Have

• Experience with Ray or PyTorch distributed training
• Background in high-performance computing (HPC) environments

🎁 Benefits & Perks

• 📈 Equity packages to share in long-term success
• 👶 Inclusive parental leave supporting all parents
• 💰 Annual wellbeing allowance for personal and professional needs
• 🏖️ Flexible leave options for rest and recharge
• 🏠 Remote-friendly working model within Australia

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

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