🛠 Tech Stack

💼 About This Role

You'll work directly with ML engineers debugging complex distributed systems and GPU infrastructure issues. Your core impact is improving reliability for large-scale AI workloads across Kubernetes and cloud platforms. This role offers exposure to cutting-edge AI infrastructure and high-impact incident response.

🎯 What You'll Do

• Diagnose distributed training and inference failures
• Troubleshoot Kubernetes, GPU orchestration, and networking issues
• Analyze logs, metrics, and traces to isolate root causes
• Build internal tooling, automation, and runbooks

📋 Requirements

• Kubernetes and containerized environments experience
• PyTorch, CUDA, or NCCL hands-on experience
• Strong Linux systems and networking knowledge
• Experience with observability tools like Prometheus/Grafana

✨ Nice to Have

• Experience with Ray, Kubeflow, or Slurm
• Familiarity with InfiniBand or high-performance networking
• Background in AI infrastructure or MLOps companies

🎁 Benefits & Perks

• 🏖️ Unlimited PTO
• 🏠 Remote-first culture
• 📈 Equity options
• 💻 Home office stipend
• 🧘 Wellness benefits

📨 Hiring Process

Estimated timeline: 2-3 weeks · AI estimate

1. 1Recruiter call· 30 min
2. 2Technical interview· 60 min
3. 3Hiring manager interview· 45 min