Senior Software Engineer – Infrastructure / Platform


Location: San Francisco, CA (In-Person)

Compensation: $250K-$350K Base + Equity + Benefits

Stack: Python, distributed systems, cloud infrastructure, event-driven architectures, data platforms, workflow orchestration, AWS/GCP, messaging systems, and large-scale Â鶹´«Ã½Ó³»­ infrastructure.


TLDR

  • Our client is building critical infrastructure and data systems that support some of the most advanced Â鶹´«Ã½Ó³»­ development efforts in the industry, operating at the intersection of data, evaluation, and frontier Â鶹´«Ã½Ó³»­ research.
  • The company has achieved exceptional early traction, serving leading Â鶹´«Ã½Ó³»­ organizations with a small, highly technical team and rapidly scaling its platform capabilities.
  • Engineers work on large-scale infrastructure challenges spanning data generation, evaluation systems, workflow orchestration, and high-throughput processing pipelines used to improve next-generation Â鶹´«Ã½Ó³»­ models.
  • This role offers broad ownership across platform architecture, infrastructure strategy, and core systems that enable both engineering and research teams to operate efficiently at scale.
  • Candidates will work closely with experienced founders and engineers while helping build foundational systems that directly influence the future of Â鶹´«Ã½Ó³»­ development.


Requirements

  • Experience building and operating distributed systems, platform infrastructure, or large-scale backend services in production environments.
  • Strong software engineering skills with Python, JavaScript/TypeScript, Go, Java, or similar backend technologies.
  • Experience designing systems in cloud environments and supporting high-throughput, data-intensive workloads.
  • Strong understanding of asynchronous processing, event-driven architectures, messaging systems, and scalable platform design.
  • Proven ability to own production systems end-to-end, including architecture, reliability, scalability, observability, and operational excellence.


Bonus Skills

  • Experience building internal developer platforms, shared infrastructure, or self-service engineering tools.
  • Experience supporting large-scale data processing systems, workflow orchestration, or distributed compute environments.
  • Experience working with Â鶹´«Ã½Ó³»­ infrastructure, model evaluation systems, machine learning platforms, or research tooling.
  • Experience at high-growth startups or other environments requiring rapid scaling and technical ownership.
  • Strong systems design skills and experience influencing architecture across multiple teams or product areas.


Responsibilities

  • Design and develop scalable infrastructure that powers data generation, evaluation systems, and large-scale platform workflows.
  • Build reliable systems capable of supporting high-throughput workloads while maintaining strong performance and operational standards.
  • Create reusable infrastructure, tooling, and platform capabilities that accelerate engineering productivity across the organization.
  • Drive architectural decisions across data pipelines, orchestration systems, compute infrastructure, and storage platforms.
  • Partner closely with engineering and research teams to enable new experimentation workflows and platform capabilities.
  • Establish engineering standards, reliability practices, and operational processes that support long-term scalability.


About

  • Our client is building foundational infrastructure that helps advance the development and evaluation of modern Â鶹´«Ã½Ó³»­ systems, supporting organizations operating at the forefront of machine learning research.
  • Infrastructure Engineers own critical platform systems that enable large-scale data operations, experimentation workflows, and production services across the organization.
  • This is a highly technical role with significant ownership, offering the opportunity to influence architecture, engineering practices, and long-term platform strategy from an early stage.
  • The role provides deep exposure to distributed systems, Â鶹´«Ã½Ó³»­ infrastructure, large-scale data platforms, workflow orchestration, and the operational challenges associated with frontier Â鶹´«Ã½Ó³»­ development.