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Mojo Debugger Engineer

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Mojo Debugger Engineer in USA new

  • Modular
  • Full time
  • Email
  • Remote

Responsibilities

  • Develop and mature the Mojo LLDB-based debugger.
  • Build interactive debugging, IDE integration, debug information formats, and expression evaluation features.
  • Potentially work on GPU debugging, agentic debugging, debugger MCP, profiling support, and other tooling features.
  • Define the Mojo debugger roadmap and overall tooling direction.
  • Collaborate with developer relations, the Mojo compiler team, the developer community, and open-source contributors.
  • Capture developer use cases, discuss features, and direct open-source contributions.

Requirements

  • Experience working in LLDB debugger internals, including maintenance, porting, or extending LLDB for a new language.
  • Knowledge of debuggers, linkers, compilers, and other development tools.
  • Ability to dive deeply into systems programming and work comfortably with ambiguity.
  • Understanding of developer experience and pragmatic feature prioritization.
  • Experience with developer communities or developer relations is strongly preferred.
  • A history of contributing to other development tools or projects is strongly preferred.

Benefits

  • Work arrangement may be office-based in Los Altos, California or remote from home within the United States.
  • New-hire onboarding is conducted in person at the Los Altos, California office.
  • Benefits may include comprehensive healthcare, retirement and savings programs, employee stock purchase opportunities, paid time off, wellbeing resources, family support, and learning and development opportunities.
  • Regular team onsites and local meetups are organized, with expected travel two to four times per year.
  • The role offers agency to define direction and work primarily in open source.

Salary: $180k - $270k/yr

Modular

Modular builds an AI developer platform for training and especially inference/serving, centered on the MAX runtime and the Mojo programming language. Its tools accelerate and deploy models from frameworks like PyTorch and TensorFlow on CPUs and GPUs, for teams running on cloud or on‑prem infrastr...

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