Technical Support Engineer

Required Hours

• Full-time schedule covering US daytime hours.
• Work both Saturday and Sunday, plus two additional weekdays.
• Four-day shift, 10 hours per day, with two additional hours of on-call coverage on Saturdays and Sundays.
• Initial Monday–Friday schedule for the first few months during ramp-up, transitioning to the weekend shift after ramping up.
• Holiday, night, and weekend support coverage may be required.

Responsibilities

• Engage directly with customers to resolve complex technical challenges involving Kubernetes GPU clusters.
• Act as a customer-facing SRE to keep customer Kubernetes clusters healthy and stable.
• Become a product expert for the GPU Cluster service and serve as the final technical escalation point before Engineering and Product.
• Monitor GPU cluster health and proactively communicate hardware issues, including thermal throttling, BMC failures, missing GPUs, and NVLink or InfiniBand degradation, with remediation steps.
• Operate and maintain production infrastructure for enterprise GPU customers, including fleet rebalancing, Slurm cluster maintenance, node repair and migration, and Kubernetes workload management.
• Investigate and resolve storage and networking issues involving Weka filesystem degradation, InfiniBand link failures, and bandwidth anomalies across bare-metal and virtual-machine environments.
• Collaborate with Engineering, Research, Product, Sales, Support, and senior internal and external stakeholders to address customer concerns and drive customer success.
• Identify patterns in support cases and work with Engineering and Go-to-Market teams to inform the product roadmap.
• Maintain documentation covering system configurations, procedures, troubleshooting guides, and FAQs.

Requirements

• 3+ years of experience in a customer-facing technical role, including 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, machine learning, GPU technologies, and their integration into high-performance computing environments.
• Advanced knowledge of infrastructure services such as Kubernetes and Slurm; infrastructure as code such as Ansible; high-performance network fabrics; NFS-based storage; container infrastructure; and scripting or programming languages.
• Experience with HPC and Slurm cluster environments, including node draining, job scheduling, and maintenance workflows.
• Familiarity with high-speed networking concepts, including InfiniBand, RDMA, and network-interface diagnostics.
• Experience with distributed storage systems such as Weka and NFS, including troubleshooting I/O and bandwidth issues.
• Foundational knowledge of installing, configuring, administering, troubleshooting, and securing compute clusters.
• Strong technical problem-solving and troubleshooting skills with a proactive approach.
• Ability to work cross-functionally, manage multiple projects, switch contexts, and prioritize effectively.
• Strong ownership, communication, and interpersonal skills, including the ability to explain complex technical concepts to nontechnical stakeholders.
• Willingness to learn new skills and operate effectively in dynamic environments.

Compensation and Benefits

• US base salary: $160,000–$230,000, plus equity and benefits.
• Competitive compensation, startup equity, health insurance, and other benefits.
• Flexible remote-work arrangements.