AI Auto-Scaling Policy Learning Engineer

About the role

Responsibilities

• Design RL-based autoscaling policy learning framework using historical demand patterns and latency feedback
• Train endpoint-specific policies optimizing cost-latency trade-offs for each traffic profile type
• Implement look-ahead scaling incorporating request queue depth and demand prediction signals
• Build policy deployment system running learned policies with reactive fallback monitoring
• Develop policy performance evaluation comparing learned versus rule-based scaling on cost and SLA metrics
• Create policy adaptation updating policies as traffic patterns shift due to product changes or user growth

Requirements

• 6+ years in ML infrastructure or systems engineering with autoscaling, resource optimization, or RL application experience
• Deep understanding of Kubernetes HPA/VPA, GPU cluster management, and inference serving latency dynamics
• Strong Python and RL implementation skills with real system control problems
• Experience deploying learned policies in production systems with safety constraints
• Track record reducing infrastructure costs while maintaining SLA compliance through optimization

Benefits

• Fully remote with flexible schedule
• Top-of-market compensation for cloud ML infrastructure expertise
• Comprehensive health benefits
• $5,000 annual professional development budget
• Equity participation

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