Technical Support Engineer (GPU Clusters) - US Weekends

About the role

As a Technical Support Engineer at a pioneering AI company, you'll be the first line of defense to support customers as they build out training, fine tuning, and inference solutions with Together AI. You'll dive deep into complex technical challenges, providing swift and effective solutions while serving as a product expert. As a part of the Customer Experience organization, you will collaborate closely with product and sales, driving continuous improvement of our offerings. This is an exciting opportunity for a deeply technical professional passionate about AI and customer success to make a significant impact in a fast-paced, innovative environment.

Required hours

This is a fulltime position working US daytime hours. The role will work both weekend days (Saturday and Sunday) as well as two additional weekdays.

This is a 4-day shift, 10 hours per day, with 2 additional hours of on-call coverage on Saturdays and Sundays.

The role would start as a Monday to Friday role for the first few months to allow for ramping up and learning from teammates. After being considered fully ramped, the role would transition to the 4-day weekend shift.

Responsibilities

Engage directly with customers to tackle and resolve complex technical challenges involving our cutting-edge Kubernetes GPU clusters; ensure swift and effective solutions every time.

Act as a customer facing SRE to ensure our customer’s Kubernetes clusters remain healthy and stable

Become a product expert in our GPU Cluster service, serving as the last line of technical defense before issues are escalated to Engineering and Product teams.

Monitor GPU cluster health and proactively communicate hardware issues to customers (thermal throttling, BMC failures, missing GPUs, and 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 seamlessly across Engineering, Research, and Product teams to address customer concerns; collaborate with senior leaders both internally and externally to ensure the highest levels of customer satisfaction.

Transform customer insights into action by identifying patterns in support cases and working with Engineering and Go-To-Market teams to drive Together’s roadmap (e.g., future models to support)

Maintain detailed documentation of system configurations, procedures, troubleshooting guides, and FAQs to facilitate knowledge sharing with team and customers.

Be flexible in providing support coverage during holidays, nights and weekends as required by business needs to ensure consistent and reliable service for our customers.

Requirements

3+ years of experience in a customer-facing technical role with at least 1 year in a support function for an AI service or 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, GPU technologies and their integration into high-performance computing (HPC) environments.

Advanced knowledge with infrastructure services (e.g., Kubernetes, SLURM), infrastructure as code solutions (e.g., Ansible) high-performance network fabrics, NFS-based storage management, container infrastructure, and scripting and programming languages.

Experience with HPC/Slurm cluster environments — node draining, job scheduling, maintenance workflows

Familiarity with high-speed networking concepts — InfiniBand, RDMA, network interface diagnostics