Senior Engineering Manager

Chegg is an innovative digital learning platform focused on providing students with affordable educational tools. They are seeking a Senior Engineering Manager to lead the architectural development of an AI-native product aimed at enhancing the college experience for students. The role involves overseeing technical architecture, managing a lean team, and collaborating closely with product and business leaders.

Responsibilities:

• 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
• Set and enforce engineering standards: code quality, security posture, observability, deployment practices, and incident response
• Anticipate the hidden risks of building fast — identify architectural debt before it becomes a production problem, and make the tradeoffs explicit
• Build systems that will keep working in production, not just systems that work in demos
• 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
• Continuously evolve the team’s AI workflows — you’re not looking for the best tool of today, you’re building the habit of always finding the best tool of tomorrow
• Build and maintain the AI-native development loop: fast context-setting, high-quality prompting, deterministic validation, and tight human-in-the-loop review
• Track what’s happening in the AI engineering tooling space and bring the best of it to the team before they ask
• 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
• Create clarity: clear priorities, clear technical direction, clear standards. The team should never wonder what 'good' looks like
• Hire for AI-native fluency — you’ll help define what that means for this team and be a key voice in recruiting decisions
• Run engineering with a product mindset: you understand that the goal isn’t working code, it’s outcomes for students
• 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
• Push back when the technical approach doesn’t match the product ambition, and propose alternatives instead of just flagging problems
• Care about the end user experience with the same intensity as you care about the codebase — you’ve shipped products that moved metrics, not just products that shipped
• Keep the team focused on the right problems. The most expensive mistake is building the wrong thing well

Requirements:

• 10+ years of software engineering experience, with 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
• 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