Technical Support Engineer (GPU Clusters) - US Weekends

Overview

As a Technical Support Engineer on the Customer Experience team, you will be the first line of technical defense for customers building training, fine-tuning and inference solutions on Together AI's Kubernetes GPU clusters, acting as a customer-facing SRE and product expert.

What you'll do

• Engage directly with customers to resolve complex technical challenges involving Kubernetes GPU clusters, ensuring swift and effective solutions.
• Act as a customer-facing SRE to keep customers' Kubernetes clusters healthy and stable.
• Become a product expert in the GPU Cluster service, serving as the last line of technical defense before escalation to Engineering and Product.
• Monitor GPU cluster health and proactively communicate hardware issues (thermal throttling, BMC failures, missing GPUs, NVLink/InfiniBand degradation) with clear remediation steps.
• Operate and maintain production infrastructure for enterprise GPU customers, including fleet rebalancing, Slurm cluster maintenance, node repair/migration, and Kubernetes-based workload management.
• Investigate and resolve storage and networking issues such as Weka filesystem degradation, InfiniBand link failures, and bandwidth anomalies on bare-metal and VM environments.
• Collaborate across Engineering, Research and Product to address customer concerns, and work with senior leaders internally and externally to ensure high customer satisfaction.
• Transform customer insights into action by identifying patterns in support cases and working with Engineering and Go-To-Market teams to drive the roadmap.
• Maintain detailed documentation of system configurations, procedures, troubleshooting guides and FAQs for team and customer knowledge sharing.
• Provide support coverage during holidays, nights and weekends as business needs require.

What you'll need

• 3+ years of experience in a customer-facing technical role, with at least 1 year in a support function for an AI service or for supporting a mission-critical API in SaaS.
• Experience as an SRE or DevOps engineer working with Kubernetes.
• Strong technical background with knowledge of AI, ML and GPU technologies and their integration into high-performance computing (HPC) environments.
• Advanced knowledge of infrastructure services (e.g., Kubernetes, SLURM), infrastructure as code (e.g., Ansible), high-performance network fabrics, NFS-based storage management, container infrastructure, and scripting and programming languages.
• Experience with HPC/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 (e.g., Weka, NFS) and troubleshooting I/O and bandwidth issues.
• Foundational understanding of installing, configuring, administering, troubleshooting and securing compute clusters.
• Complex technical problem solving and troubleshooting, with a proactive approach to issue resolution.
• Ability to work cross-functionally with Sales, Engineering, Support, Product and Research to drive customer success.
• Strong sense of ownership and willingness to learn new skills.
• Excellent communication and interpersonal skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Ability to operate in dynamic environments, manage multiple projects, and handle frequent context switching and prioritization.

Details

• Full-time position working US daytime hours.
• 4-day shift, 10 hours per day, including both Saturday and Sunday plus two additional weekdays, with 2 additional hours of on-call coverage on Saturdays and Sundays.
• Starts as a Monday to Friday role for the first few months for ramp-up, then transitions to the 4-day weekend shift once fully ramped.
• Base salary range: $160K - $230K + equity + benefits (individual compensation determined by experience, skills and job-related knowledge).
• Flexibility in remote work.