Engineering Manager, Go

Superhuman Superhuman · Consumer · Ukraine · Remote · Engineering, Product, Design, and Marketing

Engineering Manager for the Assist team within Superhuman Go, focusing on building and scaling a pervasive AI assistant that proactively surfaces agents to help users. The role involves leading a team of 8+ engineers, owning the technical direction for the agent platform, optimizing inference, and shipping user-facing products across multiple surfaces.

What you'd actually do

  1. Lead and grow a team of 8+ engineers, providing mentorship, career development, and fostering a high-performance engineering culture.
  2. Own the technical direction for the large-scale platform — from building and deploying scalable backend services that interact with AI models to optimizing inference performance to ensuring low-latency responses.
  3. Ship compelling user-facing products across our Chrome extension, native desktop application, and integrations with Superhuman Mail and Coda Docs.
  4. Collaborate cross-functionally with product, design, ML research, and platform teams to deliver an end-to-end experience.
  5. Move us from a world where users must come and ask for help to one where we proactively use our deep understanding of user activity to surface the agent that can help them most.

Skills

Required

  • engineering management experience leading teams of 5+ engineers
  • hiring, developing, and retaining strong talent
  • designing and operating scalable systems that handle complex AI workloads
  • integrate with multiple LLM providers
  • meet strict security, latency, and reliability requirements
  • shipped user-facing products at scale
  • deep appreciation for UX quality
  • bias toward getting value in front of users quickly
  • technical breadth across browser/extension development, ML/AI infrastructure, and full-stack product engineering
  • building systems that operate across diverse surfaces (browser extensions, native apps, web applications)
  • managing ambiguity — defining strategy, making tradeoffs, and shipping in fast-moving, early-stage product areas
  • Communicates clearly and influences effectively across engineering, product, design, and leadership

Nice to have

  • optimizing inference performance
  • low-latency responses

What the JD emphasized

  • proactive AI assistant
  • building the platform for these agents
  • proactive surfacing system
  • deep understanding of user activity
  • proactively use our deep understanding of user activity to surface the agent that can help them most
  • building proactive AI experiences that anticipate user needs rather than waiting for explicit requests

Other signals

  • building the platform for these agents
  • proactive AI assistant
  • understanding user context across every application
  • shipping polished user-facing experiences