Senior Engineering Manager

Chegg Inc.•Remote (CA), CA

13d•$180,000 - $285,000•Hybrid

About The Position

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.

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

Nice To Haves

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

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