Infrastructure Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

Infrastructure Engineer San Francisco | OnSite 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. -

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