Senior Engineering Manager

Chegg Chegg · Consumer · United States · Remote

Senior Engineering Manager to lead an AI-native product initiative, focusing on architectural leadership, AI agent leverage in development workflows, and managing a lean engineering team. The role requires deep experience with AI coding agents and building scalable distributed systems for AI-powered consumer products.

What you'd actually do

  1. Own the technical architecture of this product end-to-end — making the big calls on system design, data modeling, infrastructure, and how we build for scale from day one
  2. Use AI coding agents not just as productivity aids but as a core part of how the team ships — structuring problems so agents can execute effectively, guiding them when they drift, and validating what they produce
  3. Manage and grow a lean team of engineers — right now that’s two talented ICs (one in the US, one in India); your job is to make them dramatically better
  4. Partner closely with product and business leadership to translate product direction into technical strategy — you’re not a receiver of specs, you’re a co-author of them
  5. Set and enforce engineering standards: code quality, security posture, observability, deployment practices, and incident response

Skills

Required

  • 10+ years of software engineering experience
  • at least 3 years in a technical leadership or engineering management role at a product company
  • Proven architectural judgment — you’ve designed and scaled distributed systems and can speak to tradeoffs in data modeling, API design, caching, queuing, observability, and reliability
  • Demonstrably AI-native: you use AI coding agents (Cursor, Claude, Copilot, or similar) as a primary part of your workflow, not as an occasional shortcut. You should have strong opinions on how to get real engineering throughput out of them
  • Proven engineering management experience — you’ve managed engineers, set technical direction, and held a team accountable to quality and velocity
  • Product mindset: you think in outcomes, not tickets. You have enough curiosity about users to form and test your own hypotheses
  • Comfort operating in ambiguity — you’re energized by zero-to-one environments, not paralyzed by them
  • Strong written and verbal communication — you can explain architectural decisions clearly to both engineers and non-technical stakeholders
  • US-based and located in or willing to commute to Austin TX, New York NY, Chicago IL, or the Bay Area CA

Nice to have

  • Experience building AI-powered or agent-based consumer products — you understand the unique design and reliability challenges of AI-driven user experiences
  • Background in edtech, consumer internet, or products used at massive scale (millions of users)
  • Experience building and managing globally distributed engineering teams
  • A strong point of view on how AI will reshape engineering organizations over the next 2–3 years — and a willingness to act on it

What the JD emphasized

  • genuinely AI-native
  • architectural instincts
  • AI-native development loop
  • AI-native fluency

Other signals

  • AI-native engineering
  • AI coding agents
  • agentic workflows
  • technical architecture for AI products
  • managing AI-focused engineering teams