Own the full multi-region GPU infrastructure stack end to end as the sole infra hire. Build and maintain super low-latency GPU serving infrastructure. Manage multi-cloud deployments and on-premise enterprise environments.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Job Description
San Francisco, CA
- On-site
- Full-time Compensation: $150,000โ$250,000 + up to 1% equity
A seed-stage LLM interpretability and context-optimization company building custom machine learning models that analyze and compress token contexts before they reach the underlying model โ delivering roughly 50% inference cost reduction, lower latency, and higher accuracy for the enterprises and scale-ups integrating LLMs into their products. Venture-backed, with strong early traction (:1,000 customers within its first seven months).
Founded 2025
- 1โ10 people
- Industry: AI Tools / LLM infrastructure
Own the full multi-region GPU infrastructure stack end to end as the sole infra hire โ global low-latency serving, multi-cloud and on-premise deployments, reliability, and cost efficiency. Your work sits directly in the critical path of live customer traffic. This is a high-ownership, in-person role at a fast-moving early-stage team, working a 996 pace (9amโ9pm, six days a week) in San Francisco.
What You'll Be Doing
- Own the full infrastructure stack end to end across multi-region GPU deployments, major public clouds, and on-premise enterprise environments.
- Build and maintain super low-latency GPU serving infrastructure that sits in the critical path of live customer traffic.
- Manage multi-cloud deployments, including cloud marketplace integrations and provider relationships.
- Design and iterate on deployment, scaling, reliability, and cost-efficiency systems as the sole infra owner.
- Support on-premise deployments for enterprise clients and ensure performance and reliability at each site.
- Research and adopt new infrastructure solutions continuously as the stack and customer base grow.
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Requirements
- Own cloud systems serving compression API end-to-end
- Build and operate global low-latency high-throughput GPU ML inference infrastructure
- Work with AWS, Terraform, Docker and CI/CD
- Have built and operated production infrastructure at a startup or larger company
- Learn new solutions and technologies quickly
- Improve and research infrastructure solutions continuously
- Based in or willing to relocate to San Francisco to work in person at the hacker house
- Willingness to work startup hours in a 996-style environment (9amโ9pm, six days a week)
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- Quick learner who grasps products and systems fast
- Experience building for performance and reliability at scale
- Research and product focus mindset
- High ownership mentality
- Startup-minded operator who prioritizes learning and growth over work-life balance
- GPU infrastructure experience in production
- First infra hire at a startup
- Background at an infrastructure company
- Infra scope limited to model training pipelines only
- 20+ years of experience with a slow-moving, process-heavy background
- Prioritizes work-life balance as a primary requirement
- No production infra ownership
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- Sole infra owner with full-stack ownership from day one, directly in the critical path of live customer traffic.
- Well-funded seed-stage company with strong early traction and an experienced backer base.
- Significant equity, housing and food provided at the SF hacker house, visa sponsorship, laundry and cleaning, company off-sites, infinite DoorDash, and health & dental.
- Location: San Francisco, CA
- Work policy: In person (SF hacker house); 996 pace โ 9amโ9pm, six days a week
- Compensation: $150,000โ$250,000 + up to 1% equity
- Visa sponsorship: Available (H-1B, O-1, OPT)
- Employment type: Full-time
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