How to Format Your Resume for Lever

Where you'll encounter Lever

Lever is one of the most widely used hiring platforms in the startup and mid-size tech ecosystem. If you're applying to companies like Netflix, Shopify, Atlassian, KPMG, or thousands of venture-backed startups, there's a good chance your application is flowing through Lever.

Unlike legacy systems such as Taleo or Workday, which were built primarily as applicant tracking systems, Lever was designed as a talent relationship management platform -- a hybrid of CRM and ATS. That design philosophy affects how it handles your resume in ways that matter for formatting.

You can usually tell you're applying through Lever when the application URL contains jobs.lever.co or when the company's careers page has Lever's characteristic clean, minimal design with a search bar at the top and job listings below.

How Lever differs from traditional ATS platforms

Most applicant tracking systems -- Workday, Taleo, iCIMS -- were built around compliance and process management. They're designed to handle large volumes of applicants with structured data, rigid workflows, and extensive filtering. Lever took a different approach.

Lever's core philosophy is relationship-first hiring. The platform treats every candidate as a potential long-term relationship, not just an applicant for a specific role. That means:

• Candidate profiles persist across roles. If you apply to one position and aren't selected, your profile stays in the system. When another relevant role opens, recruiters can find you through search and tags -- even if you don't apply again.
• Recruiters interact with profiles, not just resumes. Lever displays a candidate overview that includes your parsed resume data, any notes from previous interactions, referral information, and source tracking.
• The system emphasizes collaboration. Hiring team members leave feedback directly on your candidate profile. Your resume is viewed in context alongside interview notes, referral endorsements, and recruiter assessments.

For you as a candidate, the practical implication is this: your resume needs to parse cleanly into structured data, because that structured data is what recruiters actually work with inside Lever.

How Lever parses your resume

Lever uses automated parsing to extract structured information from your uploaded resume. When you submit a PDF or Word document, the system attempts to pull out:

• Your name and contact information
• Work experience (company names, titles, dates, descriptions)
• Education (institutions, degrees, dates)
• Skills and technologies
• Links (LinkedIn, portfolio, GitHub)

Lever's parser is generally more capable than older systems like Taleo, but it still has limitations. It works best with standard, single-column resume layouts and struggles with the same things that trip up most parsers: tables, text boxes, headers and footers with critical information, multi-column layouts, and heavily designed templates.

What the parser handles well

• Standard section headings: "Experience," "Education," "Skills," "Projects" -- Lever recognizes these reliably.
• Reverse-chronological order: The parser expects your most recent role first and works backward.
• Consistent date formats: "Jan 2022 - Present," "2020 - 2023," "March 2019 - December 2021" all parse correctly.
• Bulleted lists: Standard bullet points under each role are parsed as job descriptions.
• PDF and DOCX files: Both work well. Lever doesn't have a strong preference between the two, though PDF ensures your formatting is preserved visually.

What causes parsing problems

• Multi-column layouts: If your resume has a sidebar for skills and a main column for experience, the parser may interleave the content or miss the sidebar entirely.
• Tables: Some resume templates use invisible tables for layout. The parser reads tables in unexpected orders, which can mix up your work history.
• Headers and footers: If your name and contact info are in the document header, the parser may not capture them. Keep your name and contact details in the main body of the document.
• Graphics and icons: Star ratings for skills, progress bars, headshot photos, and decorative icons are ignored by the parser. Worse, they can displace nearby text and cause parsing errors.
• Non-standard section names: "Where I've Worked" instead of "Experience" or "What I Know" instead of "Skills" may not be recognized as section headers.

Lever's keyword matching approach

Lever provides recruiters with powerful search and filtering tools. When a recruiter searches their candidate database, Lever looks across all parsed profile data -- your resume text, any notes, tags, and the original source.

Here's what matters for keyword optimization:

• Lever searches the full text of your resume. Unlike some systems that only search parsed fields, Lever also searches the raw text of your uploaded document. This means keywords anywhere in your resume -- bullets, summary, skills section -- are discoverable.
• Tags and stages add metadata. Recruiters can tag candidates with custom labels (e.g., "Python," "senior," "remote OK"). These tags persist across applications, meaning your profile becomes more searchable over time.
• Lever now offers AI-powered candidate ranking. In 2025, Lever launched Talent Fit, which automatically scores and ranks applicants based on how well their skills and experience match job requirements. This means your resume content is evaluated against the job description, making it more important than ever to include relevant keywords. However, Lever still emphasizes search and relationship-based hiring alongside automated ranking.

The practical takeaway: use the same language the job posting uses. If they say "project management," don't only write "PM." If they say "React," don't only write "JavaScript frameworks." Include both the specific terms and the broader categories.

The Lever application experience

Lever's candidate-facing application forms are notably simpler than many other platforms. A typical Lever application includes:

• Name, email, phone number
• Resume upload (PDF or DOCX)
• LinkedIn profile URL (optional but recommended)
• A few custom questions set by the employer (sometimes)
• An optional cover letter field

You generally don't need to re-enter your entire work history field by field, as you would with Workday or Taleo. Lever extracts that information from your resume. This is another reason your resume needs to parse cleanly -- the extracted data becomes your primary candidate profile.

One thing to note: Lever lets you apply with your LinkedIn profile instead of uploading a resume. While this is convenient, your LinkedIn profile may not be as tailored as a resume you've customized for the specific role. If you've taken the time to tailor your resume, upload it rather than relying on LinkedIn.

Formatting best practices for Lever

Based on how Lever's parser works and how recruiters use the platform, here are the formatting rules that matter:

• Use a single-column layout. This is the single most important formatting decision. One column, top to bottom. Your name, then contact info, then summary (optional), then experience, education, and skills.
• Use standard section headings. "Experience" or "Work Experience." "Education." "Skills" or "Technical Skills." "Projects." Don't get creative with section names.
• Keep contact information in the document body. Name, email, phone, LinkedIn URL -- all in the main text area, not in a header or footer.
• Use consistent date formatting. Pick a format and stick with it. "Month Year - Month Year" works well. Make sure every role has dates.
• Submit as PDF. While Lever handles both PDF and DOCX, PDF guarantees that the visual formatting you intended is what the recruiter sees. The parser works on either format.
• Include a skills section. List technical skills, tools, and domain expertise in a dedicated section. This helps both the parser and recruiters who scan quickly for specific capabilities.
• Don't use images for text. No screenshots of certifications, no logos, no skill bars rendered as images. If the parser can't read it, it doesn't exist in your candidate profile.

Common issues and how to avoid them

Duplicate profiles

Because Lever maintains persistent candidate profiles, applying multiple times to the same company can create duplicate records. If you've applied to a company before and are applying again, use the same email address. Lever uses email as the primary identifier for merging profiles.

Outdated parsed data

If you applied to a company six months ago and now have updated experience, upload a fresh resume with your new application. Lever will update your parsed profile with the new information. Don't assume the old resume is good enough -- the recruiter will see whatever you submit most recently.

Cover letter formatting

When Lever's application form includes a cover letter field, it's usually a plain text box -- not a file upload. Write your cover letter directly in the box rather than pasting from a formatted Word document, which can introduce invisible characters and broken formatting. Keep paragraphs short and skip any letterhead-style formatting.

Referral applications

Lever has strong referral tracking. If someone at the company referred you, make sure to indicate that during the application process. Referred candidates often get flagged in the system and may be reviewed faster. If the application doesn't ask about referrals, mention the referral in your cover letter.

The bottom line

Lever is one of the more candidate-friendly hiring platforms. Its applications are shorter, its parsing is more capable than legacy systems, and its search functionality means a well-formatted resume can surface you for roles you haven't even applied to. The fundamentals still apply: clean formatting, standard structure, relevant keywords, and a tailored resume beat a fancy template every time.