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Описание вакансии
Текст:
TL;DR
Software Engineer, Infrastructure (AI): Building and operating the platform foundation for compute, deployment, reliability, CI/CD, developer environments, and model training and inference with an accent on Kubernetes, GPU infrastructure, security, and cloud cost efficiency. Focus on designing reliable production systems, improving observability and deployment workflows, and shaping AI enablement infrastructure for model serving and agent tooling.
Location: San Francisco, CA or Remote, US
Base salary: $220,000–$292,000 per year, plus equity and benefits.
Company
Descript is a startup building an AI-powered video and audio editing tool for a team of approximately 150 people.
What you will do
- Own the platform foundation across GCP, Kubernetes, Temporal, GPU infrastructure, deployment, rollback, and on-call operations.
- Build and operate the infrastructure supporting model training, inference pipelines, and production AI serving.
- Improve reliability using SLOs, error budgets, observability, runbooks, targeted checks, and actionable incident response.
- Make infrastructure and cloud-cost trade-offs, including metering, attribution, capacity planning, and serving cost optimization.
- Design security boundaries covering identity and access, secrets management, least privilege, and software supply-chain integrity.
- Provide architectural direction, mentoring, technical reviews, and improvements to CI/CD, developer tooling, testing, and release practices.
Requirements
- 8+ years building and operating production distributed systems or equivalent server-side engineering with a strong infrastructure focus.
- Production experience with a major cloud provider, Kubernetes, and infrastructure as code.
- Experience operating high-consequence systems, carrying a pager, commanding incidents, and performing rollbacks.
- Experience using SLOs and error budgets as operational tools.
- Experience owning an architecture or migration from planning through launch and delivering important unowned work independently.
- Ability to use AI agents effectively while critically evaluating when and how to apply them.
Nice to have
- GPU and ML infrastructure experience, including capacity planning, training or inference pipelines, and model-serving cost and latency.
- Production security engineering involving IAM, secrets, supply-chain security, and least privilege.
- Cloud cost modeling, commitment strategies, reservations, or unit economics.
- CI/CD at monorepo scale, developer environments, or media, video, and GPU-backed workloads.
- Experience on small teams owning a broad technical surface.
Culture & Benefits
- Flexible remote work with occasional in-person collaboration opportunities.
- Generous healthcare package and 401(k) matching program.
- Catered lunches and flexible vacation time.
- Opportunity to influence the direction of an early-stage company with product-market fit.
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