Highlightai

Highlightai

Member of Technical Staff, Full-Stack

San Francisco office · Staff+

Sponsorship not specifiedDetected 434 days ago
Full-Stack DevelopmentMachine LearningAccessibilityCommunication

About the role

  • We're seeking a Member of Technical Staff to take ownership of native app aspects across the board.
  • This is a highly technical role for someone who's excited to work at the intersection of desktop software, native development, and AI integration, and who thrives in a fast-paced, collaborative environment.
  • Note: this is an on-site role, requiring five days a week in our office.

Responsibilities

  • Highlight is building a shared intelligence layer for the modern workforce.
  • Own the architecture and implementation of Highlight's native desktop runtime across macOS and Windows
  • Drive improvements to performance, stability, and resource efficiency across native processes
  • Implement robust telemetry, diagnostics, and monitoring to detect regressions and accelerate debugging
  • Collaborate with backend engineering, ML engineering, and product to deliver end-to-end features across the desktop app and backend systems

Requirements

  • 5+ years of software engineering experience, with significant experience shipping native desktop software.
  • You are a good fit if you have:
  • Hustle is expected, grit is required.

Compensation

  • Competitive salary and generous equity package

Benefits

  • Competitive salary and generous equity package
  • Health, dental, and vision insurance
  • Flexible PTO and parental leave
  • Must be based in or willing to relocate to SF - although we're flexible with days off & schedules, we have a 100% in-office culture during the week.

Company info

  • Highlight's core product depends on capturing context from the user's device, and we're looking for a software engineer to own runtime and make it production-grade: low-latency, lightweight, and highly reliable.

This listing is sourced directly from Highlightai's careers page and normalized into a canonical job model.