🛠 Tech Stack

💼 About This Role

You'll lead post-sales technical success for customers deploying large-scale AI and GPU workloads. Your core impact is ensuring predictable, cost-efficient performance for AI/ML teams. This role offers deep technical challenges in distributed training and GPU cluster optimization.

🎯 What You'll Do

• Lead onboarding for GPU clusters and workload architecture.
• Identify and resolve performance bottlenecks in GPU environments.
• Own long-term technical strategy for assigned AI accounts.
• Partner with support and engineering on incident management.

📋 Requirements

• 2-5+ years as AI/ML Engineer or TAM in GPU environments.
• Strong knowledge of GPU hardware architectures (NVIDIA/AMD) and CUDA/ROCm.
• Experience with distributed training frameworks (PyTorch, TensorFlow, DeepSpeed).
• Advanced hands-on Kubernetes and SLURM skills.

✨ Nice to Have

• Experience with hyperscale or AI lab deployments.
• Cloud Native Computing Foundation CKA certification.

🎁 Benefits & Perks

• 🏥 100% company-paid insurance for medical, dental, and vision.
• 💰 401(k) matching 100% up to 4% with immediate vesting.
• 📚 $2,500 annual professional development reimbursement.
• 🏖️ Increased PTO at 3 and 10 years + 1-month paid sabbatical every 5 years.
• 💻 $500 remote office stipend first year, $400 each following year.

📨 Hiring Process

Estimated timeline: 2-3 weeks · AI estimate

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