Platform Engineer: Internal Tools & AI Research

Roboticsconnected

San Francisco (CA)

Presencial

USD 180.000 - 240.000

Jornada completa

14 días+

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Descripción de la vacante

Roboticsconnected is seeking a seasoned software engineer to design, build, and maintain foundational frameworks and internal tooling that accelerate AI research.

You will collaborate with expert teams to validate use cases, build modular, reusable systems, and advocate for high code quality through reviews and documentation. This role focuses on delivering large-scale improvements with minimal disruption to ongoing work.

Formación

  • Excellent software engineering skills in Python, Go, or Rust.
  • Experience designing, building, and maintaining modular, reusable systems.
  • Experience collaborating with engineers and researchers from various fields.
  • Ability to implement large-impact changes with minimal disruption.
  • Experience building internal tooling for AI research and development workflows (debugging, evaluation, experiment tracking, CI automation).

Responsabilidades

  • Design, build, and maintain foundational frameworks and tools to empower expert teams to experiment fast and turn ideas into production-ready systems.
  • Collaborate with expert teams to validate uses cases and build robust solutions with modular components.
  • Identify and mitigate high-level code design flaws and workflow inefficiencies that cause friction and silos.
  • Advocate for good practices and maintain high code quality through code reviews, documentation, and training.

Conocimientos

Python
Go
Rust
Modular design
Code reviews
Documentation

Descripción del empleo

Roboticsconnected is seeking a seasoned software engineer to design, build, and maintain foundational frameworks and internal tooling that accelerate AI research.

You will collaborate with expert teams to validate use cases, build modular, reusable systems, and advocate for high code quality through reviews and documentation. This role focuses on delivering large-scale improvements with minimal disruption to ongoing work.

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