Software Engineer, Infrastructure

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

Software Engineer, Infrastructure San Francisco, California | OnSite Exa is an applied AI lab building a search engine unlike the world has ever seen. We build massive-scale infra to crawl the entire web, train state-of-the-art embedding models to process it, and design super high performant vector databases to retrieve over it. We now power search for Cursor, Cognition, HubSpot, and over 400,000 developers and have raised $350m from Lightspeed, Benchmark, and a16z. Our ultimate goal is to build perfect search over all the world's information, far beyond Google. If you want to build massive-scale ML systems that will define the way the new AI world consumes information, this is the place for you. Our Infrastructure Team builds the underlying tooling and infrastructure that powers all Exa's systems. Basically, we need more infra engineers to build the machine that builds the machine so that we can move as fast as possible as an engineering org. That could mean building GPU cluster orchestration in Kubernetes, map-reduce batchjobs on Ray, or the best observability tooling in the world. WHO YOU ARE - You have experience designing and operating large-scale infrastructure - GPU clusters or large Kubernetes clusters or cloud batchjob systems - You bring an obsessive mindset - always thinking about reliability, observability, and optimization across the entire stack. WHAT YOU'LL DO - Build the Kubernetes orchestration on a $20m GPU cluster - Scale our AWS batchjob system to handle map reduce jobs over 10s of thousands of machines - Design GPU scheduling

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