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SiftPosted 1 month ago

Senior Software Engineer, Full Stack (Agentic AI)

$140,000–$200,000 year

HybridSan Francisco, California, United States

Full TimeSenior LevelSmall

Job Summary

Talk directly to customers to translate hardware telemetry review workflows into agent capabilities, generating dashboards, analysis scripts, and surfacing insights. Design, ship, and operate agentic systems that reason over large-scale time-series data and hardware domain context. Build dependable tool interfaces, sandboxed execution environments, and custom compaction algorithms while maintaining the frontend surfaces where agents live. Develop and maintain the MCP server API that enables agents and customer tools to query telemetry directly. Run agents reliably at scale in cloud and on-prem environments, and build evaluation suites to measure agent effectiveness, quality, latency, and failure modes. Integrate and assess frontier models across providers. Work across the full stack using React, NextJS, Go, Python, or Rust.

Required Qualifications

  • 8+ years of professional software engineering experience
  • Get excited about owning a product area: talking to customers, deciding what to build, and shipping it
  • Have built frontend web applications with technologies like React, NextJS, or similar
  • Have built APIs (REST, gRPC, etc.) or backend services with technologies like Go, Python, Rust, or similar
  • Are curious about new AI products: you try new agents, models, and features as they ship, and have opinions about what makes them good
  • Must be a U.S. citizen, lawful permanent resident, or protected individual such as an asylee or refugee in compliance with ITAR (International Traffic in Arms Regulations) / EAR (Export Administration Regulations) regulations

Desired Qualifications

  • Shipped products to users at scale: large data volumes, significant active user counts, or deep technical complexity
  • Shipped LLM-powered features
  • Built agentic systems: multi-step tool use, planning loops, context management, and evals
  • Designed tool ecosystems for agents, including MCP
  • Worked with sandboxed or isolated execution of generated code
  • Operated services in production (Kubernetes, observability, incident response)
  • A personal ecosystem of AI dev tooling: custom agents, skills, scripts, or workflows built to ship faster
  • A background in time-series data, scientific computing, or hardware test and telemetry
  • Built internal agentic tooling that accelerates an engineering org

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