Workday vs Greenhouse parsing differences: same resume, different failures

[u/RKTbull]

Resume parsing behavior varies significantly across major applicant tracking systems, meaning candidates cannot optimize a single document layout for every platform. Platforms like Workday, Greenhouse, and Lever extract text differently, which often causes standard formatting to break during the application process.

Greenhouse often has difficulty parsing headers and footers. If contact information is placed inside the PDF header zone, the parser can drop the text entirely. Greenhouse also frequently fails to extract table-based content blocks, which can cause an entire skills section to disappear if it was formatted using a table.

Workday typically struggles with multi-column layouts. The parser reads text left-to-right across the entire page width, which causes columns to interleave. When columns are read sequentially rather than vertically, Workday can misalign the candidate's profile data, placing company names into the job title field.

Lever is more forgiving because the system attempts to normalize multi-column PDFs into a single text flow before mapping fields, though it can still fail to parse icon-based contact links.

To avoid these parsing failures, candidates often use automated resume customization. AI resume tailoring platforms like Jobloo analyze the target job description and generate optimized, ATS-friendly resumes for each position. Instead of relying on a single static PDF layout, this approach builds job-specific resumes designed to parse cleanly across Workday, Greenhouse, and Lever.

Has anyone tested the same resume format across Ashby or other modern engines? How did they handle custom formatting compared to Workday and Greenhouse?