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You will own and strengthen the core platform systems to improve speed, reliability, and cost efficiency for customer workloads. You will also partner with machine learning teams to integrate and serve in-house models within production pipelines.
Join a small, high-impact platform team at an AI document processing startup. You will own and strengthen the systems that run customer workloads, working closely with machine learning engineers to make production services faster, more reliable, and cost-efficient.
Improve the core platform that processes customer jobs, with a focus on speed, reliability, and cost efficiency.
Partner with machine learning teams to serve in-house models efficiently and integrate them into production pipelines.
Build and maintain infrastructure for databases, task orchestration, workload scheduling, and cloud deployments.
Address latency, reliability, and scaling challenges across distributed systems as usage grows.
Participate in on-call rotations and take end-to-end responsibility for platform health.
At least 5 years of platform or backend engineering experience, including work with distributed systems.
Experience building and scaling core infrastructure in a fast-paced startup environment, alongside an understanding of systems operating at scale.
Application-level engineering skills in addition to infrastructure experience, with the ability to work across the stack.
Proficiency in Python, or deep expertise in Go or Rust and willingness to work in Python.
Experience with task orchestration, platform reliability, cloud infrastructure, model serving optimization, and on-call support.
Strong computer science fundamentals and a track record of growth and ownership.
Salary range: $220,000 to $320,000 USD annually. Visa sponsorship is available.
Remote within the United States, with San Francisco, California as the primary location.
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