Job Description

Chegg • Austin, TX • New York, NY • Chicago, IL • Bay Area, CA • Hybrid

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.

•       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

•       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

This product team runs like a startup inside Chegg. We’ve rebuilt our development process around AI-native workflows: tight loops, minimal ceremony, and agents doing more of the implementation so humans can focus on judgment, design, and quality. The team is small on purpose. Everyone operates above their title.

This role is hybrid. We don’t require daily office presence, but you should be local to Austin, New York, Chicago, or the Bay Area and available for in-person collaboration during key sprints, architecture reviews, and team visits. Some weeks will be heads-down-in-the-office weeks, and a small amount of travel will be expected.

You’ll report directly to the CTO and work closely with the Chief Architect and product and business leaders. You’ll be highly self-directed. No one will be managing your calendar or telling you what to build each morning. Success will be judged by the quality, reliability, and velocity of what the team ships — and by how much better the engineers around you become.

The base salary pay range for this position is $180,000 to $285,000.  Although this range represents the full range for the position, the actual pay will vary within this range based on geographic location, job type, professional experience, and other factors.  

In addition, Chegg offers a comprehensive benefits plan for eligible employees, including medical, dental, vision, life and supplemental life insurance, short-and long-term disability, mental health support, parental leave, paid time off, paid holidays, 401(k) with matching contributions, Flexible Spending Account (FSA) and Health Savings Account (H.S.A.) options, an Employee Stock Purchase Plan, and other benefits found at: .