Crusoe
Senior Customer Success Manager, Managed Inference
$190K–$215K
Denver, CO - US · On-site · Manager · Full-time · Posted Jul 7, 2026
- Experience
- 3+ years supporting enterprise cloud, AI, machine learning, developer platform, or infrastructure customers; prior customer success, technical account management, or similar experience required.
- Education
- Bachelor’s degree in Business, Engineering, or a related field; advanced degree preferred.
About the job
Supports strategic AI customers running production inference workloads, helping them optimize performance, reliability, cost, and business outcomes while coordinating with internal technical teams.
What you'll do
- Manage customer relationships and advise on production inference deployment, optimization, and critical incidents.
- Monitor adoption, inference usage, latency, uptime, capacity, and business outcomes; conduct reviews and identify risks or growth opportunities.
- Coordinate customer needs with Product, Engineering, Customer Success, and Solutions teams; deliver customer training and advocate for roadmap priorities.
What they ask for
- 3+ years supporting enterprise cloud, AI, machine learning, developer platform, or infrastructure customers.
- Experience in customer success, technical account management, or a similar technology-driven role.
- Strong technical foundation in cloud computing, AI, and ML; understanding of inference workloads, model serving, Kubernetes, containers, APIs, GPU infrastructure, and AI application deployment.
- Bachelor’s degree in Business, Engineering, or a related field.
Nice to have
- Experience managing strategic accounts running production AI workloads.
- Experience with Managed Inference, model serving platforms, LLM deployments, AI agents, or GPU-based inference environments; advanced degree preferred.
Pay
Up to $190,000 – $215,000 USD OTE (total pay)
Tools
Kubernetes, containers, APIs, GPU infrastructure, LLMs, RAG
AI focus
Customer success for production AI inference, including model deployment, inference optimization, GPU utilization, autoscaling, throughput, observability, and production readiness.
Model-written summary · the original posting is authoritative