RDMA Line Controller to a Global Pharmaceutical Company

Coupang

About Coupang We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did we ever live without Coupang?” Born out of an obsession to make shopping, eating, and living easier than ever, we’re collectively disrupting the multi-billion-dollar e-commerce industry from the ground up. We are one of the fastest-growing e-commerce companies that established an unparalleled reputation for being a dominant and reliable force in South Korean commerce. We are proud to have the best of both worlds — a startup culture with the resources of a large global public company. This fuels us to continue our growth and launch new services at the speed we have been since our inception. We are all entrepreneurial surrounded by opportunities to drive new initiatives and innovations. At our core, we are bold and ambitious people that like to get our hands dirty and make a hands-on impact. At Coupang, you will see yourself, your colleagues, your team, and the company grow every day. Our mission to build the future of commerce is real. We push the boundaries of what’s possible to solve problems and break traditional tradeoffs. Join Coupang now to create an epic experience in this always-on, high-tech, and hyper-connected world.   Role Overview We are seeking a Sr. Staff Observability Engineer to lead the design and evolution of our observability platform for a GPU-as-a-Service (GPUaaS) infrastructure. This role will own the end-to-end telemetry strategy—from high-throughput metric ingestion to log pipelines and real-time visualization—powering deep insights into GPU clusters, datacenter systems, and distributed workloads. You will architect and operate planet-scale telemetry pipelines leveraging Grafana Alloy, Mimir, Loki, and Vector, ensuring high-fidelity observability across GPU workloads, Kubernetes clusters, and datacenter infrastructure.   ---------------------------------------------------------------------------------------------------------------------------------- Key Responsibilities   <Architectural Leadership & Strategy> * End-to-End Observability Platform Ownership: Design and scale telemetry pipelines using: * Grafana Alloy for metrics collection (Prometheus-compatible pipelines) * Datadog Vector for high-throughput log ingestion and transformation * Grafana Mimir for scalable time-series storage * Grafana Loki for log aggregation and querying * Strategic Roadmap: Define the multi-year vision for GPU infrastructure observability, transitioning from reactive monitoring to SLO-driven, predictive, and automated observability. * High-Cardinality Telemetry Design: Optimize pipelines for GPU workloads characterized by: * High-cardinality labels (GPU IDs, tenants, workloads) * Burst-heavy workloads (ML training, inference spikes) * Multi-tenant isolation requirements <Telemetry Pipeline Engineering> * Architect low-latency, high-throughput pipelines capable of ingesting: * GPU metrics (utilization, memory, thermals, MIG partitions) * Kubernetes and container telemetry * Distributed system logs and traces * Build and optimize metric pipelines (Alloy → Mimir) ensuring: * Efficient remote_write tuning * Cost-effective retention strategies * Horizontal scalability and compaction tuning * Design log pipelines (Vector → Loki) with: * Structured logging and enrichment * Intelligent filtering/sampling * Stream partitioning for high-ingest environments <GPU & Infrastructure Observability> * Establish deep observability into: * GPU hardware (NVIDIA DCGM, MIG, NVLink, PCIe) * Kubernetes GPU operators and scheduling behavior * Network fabric (RDMA, InfiniBand, TCP performance) * Define GPU-specific SLIs/SLOs such as: * GPU utilization efficiency * Job scheduling latency * Cluster fragmentation * Thermal and power anomalies <Visualization & User Experience> * Build rich Grafana dashboards for: * Real-time GPU fleet health * Tenant-level usage and billing insights * Capacity planning and forecasting * Standardize dashboard frameworks and reusable panels across teams * Enable self-service observability for platform and ML engineering teams <SRE, Automation & Reliability> * Drive adoption of SRE principles: * SLIs, SLOs, error budgets tailored to GPU workloads * Integrate observability into CI/CD and IaC pipelines (Terraform/Kubernetes): * Automated canary analysis * Observability-driven rollbacks * Build automation (Go/Python) for: * Pipeline health monitoring * Dynamic routing and scaling of telemetry workloads <Incident Forensics & Debugging> * Develop tooling and practices for cross-layer correlation: * GPU → Node → Kubernetes → Application → Network * Lead deep RCA efforts for: * GPU contention issues * Performance degradation in ML workloads * Telemetry pipeline backpressure/failures * Enable “needle-in-a-haystack” debugging using unified logs + metrics <Technical Leadership & Collaboration> * Mentor engineers and lead design reviews for observability systems * Act as a force multiplier across SRE, Infra, and ML platform teams * Promote Observability-by-Design in all new GPU cluster deployments <Open Source & Ecosystem Strategy> * Drive adoption and contribution to: * Grafana stack (Alloy, Mimir, Loki, Tempo) * OpenTelemetry ecosystem * Define build vs. buy decisions (Datadog vs OSS vs hybrid approaches) * Optimize interoperability between Vector and OTEL pipelines <Security & Compliance> * Architect secure telemetry pipelines with: * Encryption in transit and at rest * Multi-tenant isolation and RBAC * Data residency compliance * Implement Zero Trust observability patterns   ---------------------------------------------------------------------------------------------------------------------------------- Qualifications & Requirements * BS/MS in Computer Science or equivalent practical experience * Extensive experience in Observability, SRE, or Distributed Infrastructure * Proven track record building large-scale telemetry pipelines (metrics/logs) * Observability Stack: * Grafana Alloy / Prometheus ecosystem * Grafana Mimir (or Cortex/Thanos) * Grafana Loki * Datadog Vector (or similar log pipelines) *  Programming: * Strong in Go or Python *  Data Systems: * TSDBs and log storage at scale *  Infrastructure: * Kubernetes, Linux internals * GPU systems (NVIDIA DCGM, CUDA ecosystem) * High-performance networking (RDMA, InfiniBand preferred) * Cloud & Hybrid: * Experience building observability across: * Bare-metal GPU clusters * Hybrid cloud environments   ---------------------------------------------------------------------------------------------------------------------------------- Core Impact Success in this role is measured by: * A highly reliable, scalable observability platform powering GPU infrastructure * Ability to diagnose complex GPU and distributed system issues in minutes * Enabling data-driven optimization of GPU utilization and cost efficiency * Building systems that proactively detect and mitigate failures before user impact   ---------------------------------------------------------------------------------------------------------------------------------- Recruitment Process and Others Recruitment Process * Application Review - 1st Virtual Interview - 2nd Virtual Interview - Offer * The exact nature of the recruitment process may vary according to the specific job and may be changed due to scheduling or other circumstances. * Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage. Details to Consider * This job posting may be closed prior to the stated end date for application if all openings are filled. * Coupang has the right to rescind an offer of employment if a candidate is found to have submitted false information as part of the application process. * Those eligible for employment protection (recipients of veteran’s benefits, the disabled, etc.) may receive preferential treatment for employment in accordance with applicable laws. * Job titles and responsibilities may be subject to change depending on the candidate's overall experience, etc. this will be communicated to the candidate at the appropriate time before the offer. * Hiring may be restricted in case the legal qualifications required for hiring and work performance is not met. * This is a full-time regular position and includes 12 weeks of probation period; provided, however, the probationary period may be either skipped, shortened or extended if necessary for business purposes. Privacy Notice * Your personal information will be collected and managed by Coupang as stated in the Application Privacy Notice is located below. * https://privacy.coupang.com/en/land/jobs/ Document Return Policy 1. This notification is given pursuant to Article 11 (6) of the Fair Hiring Procedure Act. 2. A job applicant, who has applied but not been finally selected for a position at Coupang (the “Company”), may request the Company to return his/her hiring documents submitted pursuant to the Fair Hiring Procedure Act. However, this will not apply where the hiring documents were submitted via the website of the Company or e-mail, or where the job applicant submitted those documents voluntarily without a request from the Company. In addition, if the hiring documents were destroyed due to a natural disaster or any other reasons not attributable to the Company, such documents will be deemed to have been returned to the job applicant. 3. A job applicant who wishes to request the return of his/her hiring documents pursuant to the main sentence of paragraph 2 above should fill out a “Request for Return of Hiring Documents” [Annex Form No. 3 in the Enforcement Rule of the Fair Hiring Procedure Act] and submit It by email (recruitingops@coupang.com). In such case, within fourteen (14) days from the date of identifying the receipt of the request, the Company will send the hiring documents to the job applicant’s designated address via registered mail. Please be informed that the job applicant is required to pay the postage on the registered mail. 4. In preparation for a job applicant’s request for the return of hiring documents pursuant to the main sentence of paragraph 2 above, the Company shall retain the original hiring documents submitted by the job applicant for 180 days from the completion of the recruiting process. If no request is made until the end of this period, all his/her hiring documents will be destroyed immediately in accordance with the Personal Information Protection Act. 5. The above paragraphs 1 - 4 shall only apply when the labor-related laws of Korea govern the application. They are otherwise not applicable.

Coupang

Company Introduction   We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did I ever live without Coupang?” Born out of an obsession to make shopping, eating, and living easier than ever, we’re collectively disrupting the multi-billion-dollar e-commerce industry from the ground up. We are one of the fastest-growing e-commerce companies that established an unparalleled reputation for being a dominant and reliable force in South Korean commerce.    We are proud to have the best of both worlds — a startup culture with the resources of a large global public company. This fuels us to continue our growth and launch new services at the speed we have been since our inception. We are all entrepreneurs surrounded by opportunities to drive new initiatives and innovations. At our core, we are bold and ambitious people that like to get our hands dirty and make a hands-on impact. At Coupang, you will see yourself, your colleagues, your team, and the company grow every day.   Our mission to build the future of commerce is real. We push the boundaries of what’s possible to solve problems and break traditional trade-offs. Join Coupang now to create an epic experience in this always-on, high-tech, and hyper-connected world.     Role Overview We are seeking a Sr Staff System Engineer, GPU Fleet for our Coupang Intelligent Cloud (CIC) team,  to serve as the senior technical owner for our hyperscale GPU compute infrastructure. In this role, you will define fleet architecture, drive reliability and automation at scale, and lead the operation and evolution of GPU systems supporting large‑scale AI training and inference workloads. This is a hands‑on, staff‑level individual contributor role with broad technical ownership, high operational impact, and significant cross‑functional influence across hardware, infrastructure, and datacenter operations. CIC builds the infrastructure for abundant intelligence. We partner with leading AI labs, governments, and enterprises to deliver hyperscale GPU compute with high reliability, performance, and efficiency. Our infrastructure supports some of the most demanding AI training and inference workloads in production today. We operate with urgency, deep ownership, and a strong bias toward execution. Reliability, operational excellence, and rigorous systems engineering are core to our business.   What You Will Do As a Sr Staff System Engineer, GPU Fleet, you will be the senior technical owner for CIC’s large‑scale GPU compute infrastructure. This is a hands‑on senior individual contributor role with fleet‑level responsibility and broad cross‑functional influence. You will define the technical direction for how GPU fleets are architected, operated, automated, and evolved across multiple generations of hardware. Your work will directly affect fleet reliability, operating efficiency, scalability, and customer success. This role does not involve people management, but it carries principal‑level scope, autonomy, and decision‑making authority across infrastructure, hardware, and operations.   Key Responsibilities: Fleet Architecture & Technical Ownership * Own the end‑to‑end technical architecture of hyperscale GPU fleets, including hardware platform selection, firmware strategy, OS configuration, drivers, networking, and observability. * Define and enforce technical standards and best practices for fleet reliability, availability, performance, and operability. * Lead major fleet‑wide initiatives such as new GPU platform bring‑ups, multi‑generation hardware transitions, and architectural redesigns. * Evaluate trade‑offs across cost, performance, reliability, and time‑to‑deploy, and make technically sound decisions under ambiguity. Reliability, Availability & Performance * Set and drive fleet‑level reliability, availability, and performance objectives. * Lead root‑cause analysis and resolution of complex, systemic failures affecting large portions of the fleet or multiple datacenters. * Identify recurring failure patterns and drive long‑term fixes spanning hardware, software, automation, and operational processes. * Work directly with hardware vendors and partners to resolve platform‑level issues and influence future hardware designs. Automation & Systems Engineering * Design and build large‑scale automation systems for: * GPU fleet provisioning and lifecycle management * GPU health validation, diagnostics, and certification * Automated remediation, recovery, and replacement workflows * Eliminate manual operational toil through durable, well‑designed tooling that scales with fleet growth. * Ensure all fleet systems are observable, testable, and resilient under failure conditions. Operational Leadership * Act as a senior escalation point for critical production incidents impacting GPU availability or customer workloads. * Participate in on‑call rotations with a strong emphasis on preventing future incidents, not just responding to them. * Lead high‑severity post‑incident reviews and ensure learnings are translated into concrete engineering and process improvements. Technical Influence & Mentorship * Provide technical mentorship and guidance to system and infrastructure engineers across the organization. * Serve as a trusted technical partner to platform engineering, networking, datacenter operations, and leadership teams. * Influence CIC’s long‑term infrastructure roadmap through strong technical judgment and data‑driven recommendations.      Basic Qualifications  * 12+ Years of overall experience with at least 8+ years of experience in Linux systems engineering, infrastructure engineering, or datacenter operations, operating production environments with strict uptime and performance requirements. * Deep, hands‑on expertise in Linux system internals, including process scheduling, memory management, filesystem behavior, networking, kernel behavior, and system performance analysis. * Demonstrated experience operating hardware‑intensive infrastructure in production, including bare‑metal servers at scale. * Proven ability to debug complex issues across multiple system layers, including hardware components, firmware/BIOS, kernel drivers, OS configuration, and user‑space services. * Extensive experience writing production‑grade automation using Python and Bash for provisioning, configuration management, diagnostics, remediation, and fleet operations. * Strong understanding of how to design systems that are observable, resilient, and safe under failure, rather than reliant on manual intervention.   Preferred Qualifications * Direct experience operating large‑scale GPU fleets supporting AI/ML training and/or inference workloads in production. * Familiarity with modern GPU platforms and ecosystems, including GPU drivers, CUDA, NCCL, and high‑performance compute workloads. * Experience with high‑speed interconnects and datacenter networking, such as NVLink, InfiniBand, RDMA, and high‑throughput Ethernet. * Prior ownership of fleet‑wide or platform‑wide initiatives, such as new hardware bring‑ups, major architectural changes, or reliability transformations. * Experience partnering directly with hardware vendors or manufacturers to troubleshoot systemic issues or influence future platform designs. * Strong intuition for failure modes at scale, including cascading failures, correlated faults, and second‑order effects across systems. * History of acting as a technical authority or escalation point for ambiguous, high‑impact production problems. * Ability to mentor engineers through design reviews, technical problem solving, and modelling strong operational ownership. * Experience participating in on‑call rotations and responding to high‑severity production incidents with clear ownership, urgency, and technical leadership. * Strong written and verbal communication skills, including clear post‑incident reviews and technical documentation. Type of work model Hybrid Details to consider  * Those eligible for employment protection (recipients of veteran’s benefits, the disabled, etc.) may receive preferential treatment for employment in accordance with applicable laws.    Privacy Notice   * Your personal information will be collected and managed by Coupang as stated in the Application Privacy Notice located below. https://privacy.coupang.com/en/land/jobs/