Technical Support Engineer – Inference

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Together AI

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

☁️ SaaS

🏢 Enterprise

💰 $102.5M Series A on 2023-11

Artificial Intelligence • SaaS • Enterprise

Together AI is a company focused on accelerating the generative AI lifecycle by providing solutions for training, fine-tuning, and deploying AI models. They offer an end-to-end platform for leveraging pre-trained models, adapting them to specific needs, or building custom models from scratch. Together AI supports AI compute solutions with its Together Inference engine and Together GPU Clusters, allowing for scalable and efficient AI operations. Their infrastructure is powered by advanced GPUs and optimized software stacks for high performance in AI tasks.

📋 Description

• Engage directly with customers to resolve complex technical challenges involving GPU clusters, inference, and fine-tuning services • Act as a customer-facing SRE to keep customer inference endpoints on Kubernetes healthy, stable, and performant • Become a product expert and serve as the last technical defense before escalation to Engineering and Product • Assist with hardware and platform migrations by validating system health and traffic routing • Monitor dashboards, detect anomalies, and escalate with data-backed analysis • Manage customer communications during incidents and degradations • Translate technical findings into clear, evidence-backed customer updates • Execute infrastructure changes through pull requests and infrastructure-as-code for endpoint configuration, model deployment, capacity scaling, and cluster configuration • Flag engine-level bugs with logs and reproduction steps • Collaborate with Engineering, Research, Product, Sales, Support, and senior leaders to address customer concerns • Identify support-case patterns and help drive Together AI’s product roadmap • Maintain documentation covering system configurations, procedures, troubleshooting guides, and FAQs • Provide support coverage during holidays, nights, and weekends as required

🎯 Requirements

• 6+ years of experience in a customer-facing technical role, SRE, DevOps, or infrastructure engineering, including at least 1 year supporting an AI service • Experience as an SRE or DevOps engineer working with Kubernetes • Strong knowledge of AI, ML, GPU technologies, and HPC environments • Production-level experience with Kubernetes, SLURM, Ansible, high-performance network fabrics, NFS storage, and container infrastructure • Familiarity with Vast and Weka storage systems in HPC environments • Ability to diagnose complex network-layer issues and read traces • Strong knowledge of Python, TypeScript, and/or JavaScript, with curl and Postman-like testing/debugging experience • Expertise with Prometheus and Grafana at scale • Deep familiarity with REST API debugging and HTTP semantics • Experience with LLM inference frameworks, LoRA fine-tuning, and common training failure modes • Experience with Infrastructure as Code and Git-based workflows • Background in GPU cluster management • Experience with AWS, GCP, and/or Azure • Understanding of compute-cluster installation, configuration, administration, troubleshooting, and security • Complex technical problem-solving and troubleshooting ability • Ability to work cross-functionally with Sales, Engineering, Support, Product, and Research • Strong ownership and willingness to learn • Excellent communication and interpersonal skills, including explaining complex technical concepts to non-technical stakeholders • Ability to manage multiple projects, context switching, and prioritization • Ability to work US daytime hours, including Saturday and Sunday plus two additional weekdays • Ability to work a four-day, ten-hour shift with additional weekend on-call coverage • Flexibility to provide holiday, night, and weekend support

🏖️ Benefits

• Startup equity • Health insurance • Other benefits • Flexibility in terms of remote work • Competitive compensation