Senior Forward Deployed Engineer / Customer Success Engineer (GPU / AI Infrastructure)

About The Job

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Job Title

Senior Forward Deployed Engineer / Customer Success Engineer (GPU / AI Infrastructure)

Location

Seoul (Gangnam), on-site with flexible start times (8–11am). Full-time.

Company Description

A Seoul-and-San-Francisco GPU cloud ("Neo Cloud") startup that gives AI teams the GPU compute they need, when they need it — A100/H100 on demand, B200/B300/GB300 reserved. Founded in 2020, ~₩22B raised, 100+ customers including a top automaker's autonomous-driving team, a leading mobility platform's AI-agent team, academic and frontier labs, and large public-sector AI programs. Revenue grew 3.4x year over year, and a new cloud product launched in early 2026. Engineers from Google and Amazon; the first Korean team invited to present at AWS re:Invent and NVIDIA GTC.

The Role

Buying GPU compute is not like buying normal cloud. Every customer has a different model size, architecture, and training/serving setup, and what works in a demo breaks in production. The company runs a Direct-to-Expert (D2E) model: the same team handles technical validation, onboarding, production troubleshooting, and ongoing usage. As Senior CSE you own the strategic accounts that need the platform most, and you lead the CSE team so that what each engineer learns on their accounts becomes shared assets — observability standards, automation agents, solution docs.

What You'll Do

• Lead the CSE team: hiring, onboarding, growth; account assignment and support rotations; final call on escalated diagnoses; mentoring on onboarding and PoCs

• Own strategic accounts: single technical point of contact across GPU compute, high-speed fabric, and shared storage; weekly status, quarterly QBRs, executive EBRs; for new strategic customers, run first meeting → PoC success metrics → cluster delivery → performance benchmarking

• Own the issue lifecycle: decide whether a failure is platform, customer environment, or hardware; run escalation through RCA; judge hardware faults, own RMA decisions and customer communication, coordinate replacement with SRE/infra

• Automation & playbooks: move recurring diagnosis/response patterns into runbooks and AI-agent-based tooling; prioritize the team's automation backlog; turn recurring platform issues into product improvements and track them to resolution

Who You Are

• 5+ years in customer-facing technical roles, including 2+ years as a TAM / Solutions Architect on AI or HPC infrastructure

• You have explained outages to executive customers and driven decisions

• You have led or mentored a team

• Hands-on with at least one of: GPU cluster operations (nvidia-smi, DCGM, hardware fault triage, RMA workflows) or diagnosing performance bottlenecks in distributed training / inference serving (communication, storage, inference-engine config)

• Linux troubleshooting from logs and system state, written up as RCA documents

• Production code in Python or Go

Even better

• MSP partnerships or dedicated-account technical support

• Designed diagnostic/monitoring tooling and got a team to adopt it

• InfiniBand/RoCE fabrics or parallel file systems (VAST, WEKA, Lustre)

• Automated repetitive work with AI coding tools or agents and measured the impact

• Tuned vLLM, SGLang, or TensorRT-LLM in production

Process

Resume interview (1h, with the team's senior leader) → take-home assignment → deep-dive/culture interview (up to 3h, on your assignment) → CEO and CTO interviews (30 min each) → reference check. Full-time with a 3-month probation period.

Why Join

• Be the person who makes GPU compute actually work for Korea's most demanding AI teams, at one of the fastest-growing AI infrastructure companies in the region

• Lead a team from the ground up and define how customer engineering scales

• Annual US market exposure trip, self-development budget, health checkups, flexible hours, long-service refresh leave