Technical Support Engineer (GPU Clusters) - US Weekends
$160k - $230k • Remote
Posted 10h ago
Job Location
Remote
Tech Stack
Remote Work Policy
On-site
Categories
AI Infrastructure Engineer
About the job
As a Technical Support Engineer at a pioneering AI company, you will be the first line of defense supporting customers building training, fine-tuning, and inference solutions. You will address complex technical challenges, provide effective solutions, and act as a product expert. Collaborating with product and sales teams, you will contribute to the continuous improvement of offerings. This role is for a technically proficient individual passionate about AI and customer success, seeking to make a significant impact in a fast-paced, innovative environment.
Responsibilities
- Resolve complex technical challenges for customers using Kubernetes GPU clusters.
- Act as a customer-facing SRE to ensure Kubernetes cluster health and stability.
- Become a product expert for the GPU Cluster service, serving as the final technical escalation point.
- Monitor GPU cluster health and proactively communicate hardware issues with remediation steps.
- Operate and maintain production infrastructure for enterprise GPU customers, including fleet rebalancing and workload management.
- Investigate and resolve storage and networking issues on bare-metal and VM environments.
- Collaborate with Engineering, Research, and Product teams to address customer concerns.
- Translate customer insights into actionable feedback for product roadmap development.
- Maintain detailed documentation of system configurations, procedures, and troubleshooting guides.
- Provide support coverage during holidays, nights, and weekends as needed.
Requirements
- 3+ years in a customer-facing technical role, with at least 1 year supporting an AI service or mission-critical SaaS API.
- Experience as an SRE or DevOps engineer working with Kubernetes.
- Strong technical background in AI, ML, GPU technologies, and HPC environments.
- Advanced knowledge of infrastructure services (e.g., Kubernetes, SLURM) and infrastructure as code solutions (e.g., Terraform).