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We’re Looking for engineers who run production AI/ML workloads to better understand how teams manage compute, cost, and reliability.
This is not a job application, it’s a short 30-minute research interview.
Who we are looking for:
Engineers who have hands-on experience running AI workloads on GPUs, especially across cloud environments.
Typical roles include:
• ML Engineer
• AI Engineer
• Platform Engineer (AI/ML)
• MLOps Engineer
• Infrastructure Engineer supporting AI workloads
Experience we are particularly interested in:
• Running model training or inference on GPUs
• Working with providers like AWS, GCP, Azure, CoreWeave, Lambda, RunPod, etc.
• Managing Kubernetes / containers for ML workloads
• Dealing with GPU availability, cost optimization, or infrastructure reliability
The conversation will focus on:
• How you runs AI workloads today
• How you choose GPU providers
• What breaks or causes friction in production environments
• How teams manage cost and reliability
Key Skills
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