Cursor VP of Engineering on Writing Resumes That Actually Get Seen

I recently came across a post from Cursor VP of Engineering Lee Robinson about engineering job applications (original thread). Drawing from reviewing hundreds of resumes, he outlined several patterns that make candidates more likely to get noticed.

His points were simple but easy to miss: keep your resume to one page, use a personal site for overflow detail, make GitHub demonstrate real engineering ability (not decoration), prioritize portfolio quality over quantity, tailor your resume to each company, and explicitly show AI or agent-tool workflow experience. He also noted that many resumes still skip AI entirely, which can make candidates look behind current industry shifts.

A lot of what Robinson emphasized is also covered in our previous course, Software Engineer Job Search Playbook. If you are interested, check out our E+ Growth Program. In addition to job-search lessons, E+ includes resume review/edit support and career Q&A channels.

Key Resume Advice from Lee Robinson

Here is a breakdown of the major points from his post.

Keep your resume to one page

A resume should generally stay within one page. If you have more to share, publish it on your personal site and link out. Each experience section does not need a long list of bullets. The goal is not to include everything you have ever done; it is to make your strongest signal obvious in seconds.

This aligns with what we covered in Software Engineer English Resume Guide: recruiters and hiring managers usually do not have time for multi-page resumes. Even senior engineers often present highlights in one page.

Robinson also recommends avoiding photos on resumes. If you want one, keep it on your personal site or external profile. The resume itself should reserve space for experience, capability, and outcomes. That said, this can vary by region. Since Cursor is US-based, this guidance fits US hiring norms; in some European markets, photos may still be expected. Localize based on where you are applying.

Invest in your personal site and GitHub

Robinson argues that a thoughtfully curated personal site can put you ahead of most applicants. The keyword is thoughtful. A rough, generic, AI-looking site can hurt more than help.

He also strongly recommends including GitHub. But avoid turning your profile README into a badge-heavy decorative page. Hiring managers are not there for visual flair. They want to understand how you write code, structure problems, and ship real ideas.

The same principle applies to social links. If you include social profiles, clean them up first. It sounds obvious, but public posts shape first impressions quickly.

And while many developers spend most of their time on X, LinkedIn still matters in internal candidate handoffs. Your LinkedIn does not need to read like a corporate brochure, but it should quickly answer: who you are, what you have done, and what you are currently focused on.

Tailor your resume to each target company

Robinson also emphasizes tailoring. For startups, your course list is usually less important than your ability to build fast, understand product context, and solve messy problems. For larger tech companies, especially where ATS filtering is strict, keyword alignment and standardized experience formatting can matter more.

On AI, Robinson said he was surprised by how many engineering resumes still do not mention AI or agent tooling. Today, a reasonable expectation is that engineers know how to use AI in coding, debugging, planning, or accelerating delivery workflows. That should show up in your resume, portfolio, or project writeups. It is not enough to claim “familiar with AI tools.” Show how AI is integrated into your actual workflow.

If you want stronger examples of AI workflow presentation in resumes, see our course AI Coding 201: From Practice to Best Practices. After applying those methods at work, your resume bullets might look like this:

• Built a reusable team-level agent skill library, packaging domain knowledge and execution patterns so XX engineers could leverage established workflows immediately, reducing knowledge-transfer overhead by YY%.
• Defined and enforced a cross-team ubiquitous language glossary across frontend, backend, and product, eliminating XX naming ambiguities and reducing AI agent retrieval misses caused by inconsistent terminology.
• Migrated linting and formatting toolchains to higher-performance alternatives, reducing lint time from XXs to under YYs and formatting time from XXs to under YYs, directly speeding up AI agent feedback loops at scale.

Prioritize quality, and avoid generic AI voice

One especially interesting point from Robinson: do not let AI fully write your resume or cover letter. Even as an executive at a leading AI company, he argues the final wording should still be authored by a human.

Use AI to brainstorm, organize raw material, and compare phrasing. But write the final text yourself. Avoid overused formulaic language that immediately reads like default AI output. Your writing does not need to be fancy; it needs to sound real and specific to you.

Beyond writing quality, your resume and portfolio should communicate your taste, strengths, and interests. They are not just proof that you can code; they are signals of how you think.

His final point: for portfolio projects, quality beats quantity. Three thoughtful, detailed projects with clear decisions and outcomes are far more convincing than twenty-seven shallow, template-like projects with heavy AI sameness.

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