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Software Engineer, System Enablement
OpenAI — один из самых престижных работодателей в мире ИТ на данный момент. Роль предлагает уникальный опыт работы с передовым оборудованием и влияние на развитие глобальной инфраструктуры ИИ, что гарантирует колоссальный профессиональный рост.
Сложность вакансии
Высокая сложность обусловлена необходимостью глубоких знаний на стыке железа (UEFI, BMC, PXE) и облачных технологий (Kubernetes, IaC). Работа в OpenAI предполагает экстремальные нагрузки и решение уникальных задач по масштабированию, для которых нет готовых инструкций.
Анализ зарплаты
OpenAI обычно предлагает компенсацию выше среднего по рынку Сан-Франциско, включая значительные пакеты акций (PPU). Указанные рыночные цифры отражают базовую зарплату для Senior-уровня, но совокупный доход в OpenAI может существенно их превышать.
Сопроводительное письмо
I am writing to express my strong interest in the Software Engineer, System Enablement position at OpenAI. With over five years of experience in systems software and infrastructure automation, I have a proven track record of transforming raw hardware into production-ready fleet capacity. My background in managing Kubernetes node lifecycles and developing robust provisioning workflows using Terraform and Python aligns perfectly with the Scaling team's mission to build the architectural backbone for OpenAI’s infrastructure.
In my previous roles, I have specialized in the 'messy' phase of hardware bring-up, from debugging PXE boot issues and UEFI configurations to implementing automated health checks and telemetry. I am particularly drawn to this role because it sits at the critical intersection of hardware and cloud-scale software. I am eager to bring my expertise in golden image creation and multi-cloud operations to ensure that OpenAI’s next-generation compute resources are stable, observable, and ready for the most demanding AI workloads.
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Описание вакансии
About the Team
The Scaling team is responsible for the architectural and engineering backbone of OpenAI’s infrastructure. We design and deliver advanced systems that support the deployment and operation of cutting-edge AI models. Our work spans system software, networking, platform architecture, fleet-level monitoring, and performance optimization.
About the Role
We’re looking for an engineer who can take early, sometimes messy, pre-production hardware and make it “real”: bootstrapped, stable, imaged, joined to the right Kubernetes control plane, registered correctly, scheduled, and observable. You’ll sit at the intersection of early HW bring-up, provisioning automation, fleet/cluster management systems, and lab or cloud provider integration—turning new SKUs into capacity that is usable by internal customers.
Key Responsibilities
- Own the end-to-end bring-up and bootstrap path for new systems and compute nodes from bare metal/early access in lab or production/cloud environments to schedulable fleet capacity: image build, user-data/config, cluster join, and readiness gates.
- Build and maintain “first-class” golden image + provisioning workflows across lab, and production environments, including working with partner-provided base images and reconciling OS/version requirements.
- Work with partner teams to integrate nodes into our fleet infrastructure and IaC pipelines (Terraform, Chef, etc.), ensuring cloud resources map cleanly onto our internal lifecycle expectations (e.g., VMSS/instance pools, image references).
- Partner with scheduling and platform owners to ensure new hardware is reachable and scheduled (pool definitions, network/WAN connectivity/routing, admission controls, platform-specific quirks), including cases where new SKUs require changes for scheduling integration.
- Drive registration and inventory correctness (e.g., systems that track nodes and their metadata), including hands-on support to get nodes registered and visible end-to-end.
- Collaborate with partner teams to implement baseline health + telemetry bring-up: minimum viable health signals, pass/fail checks, and automated reporting suitable for early ramp decisions
- Debug issues across layers: PXE/boot-loader, UEFI/BIOS, BMC, OS bring-up, NIC/network reachability, kubelet/control-plane connectivity, storage constraints, and early rack/lab realities.
Qualifications
- BS in CS/EE (or equivalent practical experience).
- 5+ years of experience in systems SW development and building/operating Linux-based infrastructure in production or pre-production environments.
- Strong, hands-on experience with:
+ Kubernetes cluster operations (node lifecycle, bootstrap/join, debugging control-plane connectivity)
+ Infrastructure-as-Code / config management (Terraform, Chef/Ansible, etc.)
+ Provisioning and imaging (PXE/iPXE, golden images, cloud-init/user-data)
+ Networking fundamentals (L2/L3, routing, DNS, fire-walling; comfort debugging reachability
- Proven ability to write automation in Python/Go/Bash and ship operational tooling/run-books.
Preferred Skills
- Experience bringing up new hardware platforms (early silicon/servers/NICs) in a lab setting and turning them into stable fleet capacity.
- Multi-cloud operational experience (Azure/GCP/AWS/OCI), especially with compute pools (e.g., VMSS / instance pools).
- Experience building telemetry/health pipelines (agent-based metrics/logging, health rollups, readiness criteria).
- Familiarity with WAN, peering, and multi-site network concepts for cluster deployments.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.
We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.
OpenAI Global Applicant Privacy Policy
At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
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Навыки
- AWS
- Azure
- Python
- Linux
- Terraform
- GCP
- Kubernetes
- Bash
- Networking
- Go
- Ansible
- DNS
- Chef
- BIOS
- UEFI
- PXE
- OCI
Возможные вопросы на собеседовании
Проверка опыта работы с низкоуровневой загрузкой и отладки проблем на этапе инициализации железа.
Опишите ваш процесс отладки, если новый узел не может загрузиться по PXE в изолированной сетевой среде. С чего вы начнете и какие инструменты будете использовать?
Оценка навыков автоматизации и понимания жизненного цикла узлов в K8s.
Как бы вы спроектировали систему автоматического присоединения (bootstrap) новых узлов к кластеру Kubernetes, чтобы минимизировать ручное вмешательство и обеспечить безопасность?
Проверка умения работать с инфраструктурой как кодом в контексте управления парком серверов.
Какие стратегии управления 'золотыми образами' (golden images) вы считаете наиболее эффективными при работе с несколькими облачными провайдерами одновременно?
Оценка понимания сетевых протоколов и их влияния на стабильность кластера.
С какими проблемами маршрутизации L3 или работы DNS вы сталкивались при масштабировании флота до тысяч узлов, и как вы их решали?
Проверка навыков мониторинга и обеспечения надежности.
Какие метрики 'здоровья' узла вы считаете критически важными для автоматического вывода сервера из эксплуатации (drain) до того, как он вызовет сбой в работе моделей?
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