Relace
Infrastructure Engineer
San Francisco
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- Seniority
- Mid level
- Country
- US
- Work mode
- On-site / unstated
- First seen by hirly
- 28 Sept 2026
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the posting
About Us
Relace is building the models and infrastructure that code agents reach for. We power the fastest model on OpenRouter (10,000 tok/s) and deliver optimized small language models designed for retrieval, application, and core code generation functions.
Our technology supports some of the world’s fastest-moving companies — including Lovable, Figma, and Vercel — as they deploy and scale code generation to hundreds of millions of users. We recently raised our Series A from a16z, and we’re growing quickly.
Our team is made up of mathematicians, physicists, and computer scientists who are deeply passionate about their craft. If you thrive on ambitious technical problems, care about elegant systems design, and want to build the foundation of how code gets written at scale, this is the place for you.
The Role
As an Infrastructure Engineer at Relace, you’ll design and operate the systems that power our high-performance inference and training infrastructure. You’ll work closely with our research and product teams to ensure our models run at scale with reliability, speed, and cost-efficiency. This is a hands-on engineering role where you’ll shape how we build and scale the backbone of modern code generation.
You’ll have the opportunity to:
- Architect and manage the infrastructure powering our ultra-fast inference and training stack.
- Build reliable, efficient systems for deploying and scaling ML workloads globally.
- Work on GPU scheduling, distributed systems, and high-performance cloud deployments.
- Optimize performance and cost across compute, networking, and storage layers.
- Collaborate with world-class engineers to push the limits of what small models can do.
Requirements
2+ years of experience writing high-quality production code
Strong experience with cloud infrastructure (AWS, GCP, Azure, or equivalent)
Experience with data science and systems optimization
Familiarity with ML infrastructure, GPU’s, etc. a plus
Work out of our SF office in FiDi
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