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Infrastructure Engineer 1More

8M JPY - ***M JPY

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Kanagawa

Full-time (permanent)

Online Interview: Yes

Remote Work from Overseas: Yes / Remote Work in Japan: Yes

Top-Selling Points

Rate of Foreigners

Less Than

Frequency of English Usage

Less Than

International employees Apply from overseas Remote work

Related Skills

Cloud / Infrastructure / DevOps tools

Kubernetes Docker

Operating System

Linux

Language Requirements

Required

Preferred

Job Description

[Recruitment Background]
With AI and machine learning set to become the foundation of society, the company aims to lead the world with next-generation AI cloud services. A strategic partnership with Tenstorrent, led by renowned semiconductor designer Jim Keller, marks the full-scale expansion into the global market. The mission extends beyond technical provision to driving client business success through innovation. The team seeks engineers to help transform cutting-edge AI chip technology into accessible infrastructure for everyone.

[Role overview]
As an Infrastructure Engineer, you will own the GPU platform that runs production inference: 
cluster architecture, deployment reliability, observability, capacity management, and incident 
response mechanisms. Your job is to make the platform predictable and reliable—even as we 
scale hardware, models, tenants, and traffic patterns.
You’ll work closely with serving/runtime and gateway teams to ensure the platform enforces the 
right isolation, exposes the right telemetry, and supports safe changes without downtime. This 
role blends strong systems intuition with real production discipline: reliable rollouts, clean 
operational tooling, and fast incident response.

[Responsibilities]
● Own GPU cluster architecture and operations: provisioning, node images, 
driver/runtime lifecycle, GPU plugin/operator lifecycle, and standardized deployment 
patterns for serving pools and system services.

● Define and maintain the production baseline: golden node configurations, cluster 
hardening, upgrade paths, and “known good” compatibility matrices (drivers ↔ CUDA ↔
runtime ↔ kernel).

● Build reliability into the platform: SLOs/SLIs, alerting quality, runbooks, incident 
tooling, and postmortems with real follow-through (automation, guardrails, and 
elimination of repeat incidents).

● Enable safe delivery: canary deploys, progressive rollouts, rollback paths, and 
configuration safety (validation, guardrails, change controls, and safe defaults).

● Own fleet health and maintenance workflows: node draining, GPU quarantining, 
automated remediation, scheduled maintenance, and safe “break-glass” procedures with 
auditability.

● Capacity and utilization: scheduling constraints, binpacking/fragmentation 
management, warm pools, autoscaling primitives, and quota enforcement hooks that 
align with product tiers and fairness goals.

● Observability: metrics/logs/tracing across gateway → serving → GPU; latency 
breakdowns, saturation signals, queue depth, GPU memory/compute metrics, and fleet 
health dashboards that help correlate customer symptoms to root causes.

● Production readiness for heterogeneous environments: manage differences across 
hardware generations and evolving server platforms, minimizing reliability risk while 
improving utilization.

● Security baseline: secrets management, least-privilege access, audit trails for operator 
actions, and secure operational workflows.

● Partner with networking: topology, failure domains, load balancing, and performancesensitive traffic paths that impact tail latency and availability.

● Build operational tooling: fleet management, debugging workflows, safe admin 
actions, capacity tooling, and maintenance automation that reduces MTTR and improves 
operator efficiency.

● Collaborate across teams: align rollout plans, health semantics, capacity signals, and 
failure handling so the entire platform behaves predictably under load.

[Development Environment]
Primary Languages: Go, Python, Bash (for operations automation and tool development)
Frameworks: Kubernetes (Container Orchestration), GPU Operator, K8s Plugins (Nice to have: Envoy or other service meshes)
Database: Time-series DB and logging infrastructure for metrics and logs
Cloud Services: Kubernetes-based GPU cluster environment (Multi-node/Multi-tenant configuration)
Others: Linux, Docker, CI/CD, Monitoring/Observability infrastructure (e.g., OpenTelemetry), Alert/SLO management tools, Incident management and automation tools

[Impact and Achievements of Global Employees]
Foreign nationals from countries such as Sri Lanka, Uzbekistan, and Canada comprise half of the workforce. Both English and Japanese are used daily within the company.

[Work Environment]
Full remote work is available. Some employees work entirely remotely from locations outside of Japan.

Required Skills and Experiences

[Requirements] ● 5+ years in infrastructure/SRE/platform engineering for production distributed systems. ● Strong Kubernetes experience in production (or equivalent orchestration), with real ops ownership. ● Experience operating GPU clusters or other high-performance compute fleets (or similarly performance-sensitive infrastructure). ● Strong debugging skills across Linux, networking, and distributed systems failure modes. ● Strong operational discipline: automation-first mindset, measurable reliability, careful change management, clear communication during incidents. ● Willing to participate in an on-call rotation for owned systems [Nice to have] ● Experience with high-throughput gateways/service meshes (e.g., Envoy), OpenTelemetry, and multi-region architectures. ● Experience with Slurm/HPC-style scheduling, RDMA/IB, or performance-sensitive networking. ● Experience building internal developer platforms and “golden paths” for consistent deploy/rollback workflows. ● Experience managing GPU driver/runtime upgrades safely across a fleet (compatibility testing + staged rollouts). ● Familiarity with observability patterns for latency-sensit

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Working Condition

[Employment Type]
Permanent employee. Probation period: 3 months.

[Work Location]
*Full remote work is available. Some employees work remotely from locations outside of Japan.
■Address: 10F Queen's Tower A, 2-3-1 Minatomirai, Nishi-ku, Yokohama-shi, Kanagawa 220-6010
■Access:
7-minute walk from Sakuragicho Station (all lines).
Directly connected to Minatomirai Station (Minatomirai Line / Tokyu Toyoko Line).
■Smoking Policy: No smoking allowed on the premises (Entirely non-smoking).
■Scope of Change in Workplace: Any workplace designated by the company.
■Transfer/Secondment: None (No relocation or external secondment required).

[Expected Annual Salary]
Annual Salary: JPY 8,000,000 – 14,000,000
Monthly Salary: JPY 666,000 – 1,166,000
*Base Monthly Salary: JPY 666,000 – 1,166,000
■Salary Increase: Available (Based on performance review)

[Working Hours]
・Working Hours: 09:00 – 18:00
・Break Time: 60 minutes

[Benefits and Welfare]
Commuting Allowance: Partially covered (Up to JPY 15,000 per month). / Social Insurance: Fully covered (Health Insurance, Employee Pension, Employment Insurance, and Workers' Accident Compensation Insurance). / Overtime Pay: Paid according to actual overtime hours worked.

[Holidays and Leave]
Annual Holidays: 120 days / Five-day Workweek System / Annual Paid Leave: 10+ days (Starting from the 7th month of employment)

RECRUITMENT PROCESS

HR interview ⇒ departmental interview ⇒ technical interview ⇒ final interview ⇒ offer
*The number of times and contents of the selection process (with or without live coding, etc.) may be subject to change depending on the situation.

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