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Job Description
Job Listing ID:
4498592
Job Title:
Senior Engineering Manager Application Deadline:
06/25/2026
Job Location:
Salem
Date Posted:
05/26/2026
Hours Worked Per Week:
40
Shift:
Day Shift Duration of Job:
Full Time, more than 6 months You may contact this employer directly.
(Obtain the contact information to print or add to your jobs.)
Job Summary:
Job Description
AI-Native Engineering Lead 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
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