Chegg • Austin, TX • New York, NY • Chicago, IL • Bay Area, CA • Hybrid
The Role
We're building a deeply AI-enabled product designed to give every college student an unfair advantage in the moments that matter most. This is Chegg's single most transformative internal initiative: zero-to-one, live in market, and moving fast. The team is deliberately lean. Every person on it operates at a level well above their title.
This is not a traditional engineering leadership role. We need someone who is genuinely AI-native - not AI-assisted - and who has the architectural instincts to keep our foundations sound as agents build faster on top of them. You will own the engineering craft end-to-end: deciding how we build, keeping the system honest, orchestrating AI tooling to multiply throughput, and growing the engineering capability of the team over time.
You will report directly to the CTO and work closely with our Chief Architect, who brings 20+ years of experience and is one of the most effective AI-augmented engineers we have encountered. Your trajectory on this role is to eventually step into the Engineering leader position for this product and manage it's lifecycle.
You will collaborate closely with our product and business leaders, and a small team of engineers in the US and India. The pace is startup speed. The stakes are real.
What you'll do
Architecture & technical leadership
• 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
AI-native engineering & agent leverage
• 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
Engineering management & team growth
• 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
Product & outcome taste
• 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
Required Qualifications
• 10+ years of software engineering experience, with at least 3 years in a technical leadership or engineering management role at a product company