Key Takeaways

  • 75% of U.S. employers use automated applicant tracking systems to screen resumes before a human reviews them (Harvard Business School & Accenture, 2021)
  • The most common ATS failures are missing keywords, incompatible formatting, and incorrect file types
  • ResumeGeni scores your resume across 8 parsing layers — modeled on the same steps enterprise ATS platforms like Workday, Greenhouse, and Taleo use to evaluate candidates

How ATS Resume Scoring Works

Applicant tracking systems parse your resume into structured data — extracting your name, contact info, work history, skills, and education — then score how well that data matches the job requirements. Many ATS rejections happen because the parser couldn't extract critical fields, not because the candidate wasn't qualified.

LayerWhat It ChecksWhy It Matters
Document extractionFile format, encoding, readabilityCorrupted or image-only PDFs fail immediately
Layout analysisTables, columns, headers, footersMulti-column layouts break field extraction
Section detectionExperience, education, skills headingsNon-standard headings cause sections to be missed
Field mappingName, email, phone, dates, titlesMissing contact info is a common cause of immediate rejection
Keyword matchingJob-specific terms, skills, certificationsKeyword overlap affects recruiter search visibility and ATS scoring
Chronology checkDate ordering, gap detectionReverse-chronological order is expected by most ATS
QuantificationMetrics, numbers, measurable outcomesQuantified achievements help human reviewers and some scoring models
Confidence scoringOverall parse quality and completenessLow-confidence parses get deprioritized in results

Frequently Asked Questions

Is ResumeGeni free?
Yes. ResumeGeni is currently in beta — ATS analysis, scoring, and initial improvement suggestions are free with no signup required. Full guidance and saved reports may require a free account.
What file formats are supported?
PDF, DOCX, DOC, TXT, RTF, ODT, and Apple Pages. PDF and DOCX are recommended for best ATS compatibility.
How is the ATS score calculated?
Your resume is processed through an 8-layer parsing pipeline that extracts structured data the same way enterprise ATS platforms do. The score reflects how completely and accurately your resume can be parsed, plus how well your content matches common ATS ranking criteria.
Can ATS read PDF resumes?
Yes, but not all PDFs are equal. Text-based PDFs parse well. Image-only PDFs (scanned documents) and PDFs with complex tables or multi-column layouts often fail ATS parsing. Our analyzer will flag these issues.
How do I improve my ATS score?
Focus on three areas: use a clean single-column format, include keywords from the job description naturally in your experience bullets, and ensure all sections (contact, experience, education, skills) use standard headings.

ATS Guides & Resources

Built by engineers with 12 years of experience building enterprise hiring technology at ZipRecruiter. Last updated .

Staff Platform Engineer

Aerovect · Remote

Who We Are

AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers. For more information, visit www.aerovect.com.

As a Staff Platform Engineer at AeroVect, you will drive the technical direction and long-term architecture of the software foundation that powers our autonomous ground vehicle fleet. You will be the most senior individual contributor on the platform team, accountable for the reliability, performance, scalability, and safety posture of our Ubuntu-based OS images, real-time middleware, and device driver layer that interface with a diverse multi-sensor stack. You will help setting the multi-year roadmap for the platform, make the build-vs-buy and architecture decisions that shape how the rest of engineering builds on top of it, and raise the technical bar across the organization by growing senior engineers and help establishing strong engineering standards. Your work enables our perception, autonomy, and controls teams to iterate rapidly and deliver safe, production-ready capabilities to customers worldwide.

You Will

  • Define the technical vision and multi-year roadmap for the platform, including how we evolve from today's fleet to the next order-of-magnitude in scale, sensor count, and autonomy maturity.

  • Own the future architecture of our Ubuntu/Yocto/Linux distributions tailored for real-time, safety-critical autonomous vehicle workloads, and make the build-vs-buy decisions that follow.

  • Set the strategy for ROS 2 IPC middleware (Cyclone DDS, Fast DDS, Zenoh, etc.) across the fleet — including profile selection, QoS standards, and determinism budgets for multi-sensor data flows.

  • Lead development of user-space drivers for LiDARs, cameras, radars, GNSS/INS, CAN, and other vehicle interfaces, and set the standards other engineers follow when adding new hardware.

  • Own the platform's functional safety and security strategy end-to-end — secure boot, OTA update pipelines, CVE response, and alignment with ISO 26262 / SOTIF workflows as we mature toward production.

  • Define the observability contract for the platform: what "healthy" looks like in the lab and in the field, and the SLIs/SLOs the autonomy and perception teams can build against.

  • Collaborating with autonomy, perception, and controls leads to set cross-stack performance budgets (CPU, GPU, memory, bus bandwidth, end-to-end latency) and drive the cross-team work to hit them.

  • Set standards for how the platform is built, tested, and released — CI/CD for OS images and driver packages, hardware-in-the-loop testing, release gates, and rollback strategy.

  • Contribute to the platform team's technical hiring and calibration — own the interview rubric, grow senior engineers into tech leads, and raise the bar on code review and design review across the team.

  • Represent AeroVect technically in relationships with silicon, sensor, and middleware vendors, and influence their roadmaps where it matters to us.

  • Provide on-call escalation support for platform components during field trials and customer pilots, and use what you learn in the field to drive systemic fixes.

  • Identify strategic technical debt and drive it down — not just within the platform, but stack-wide.

You Have

  • 10+ years of experience developing Linux-based embedded or robotics platforms.

  • Built a platform from 0→1 at a robotics/AV company that scaled through at least one order of magnitude in fleet size.

  • Demonstrated ability to lead technically across multiple teams without direct reports — aligning autonomy, perception, and hardware stakeholders on a shared platform direction.

  • Demonstrated experience growing senior engineers and raising engineering standards (design review, code review, on-call practices, post-mortems).

  • Expert-level C++ (≥C++17) and strong Python, with deep instincts for which language belongs where in a real-time system.

  • Deep, first-hand experience shipping real-time, safety-critical systems — Avionics, AV/ADAS, or comparable domains — not just general embedded Linux.

  • Deep knowledge of IPC middlewares and techniques on Linux/POSIX systems.

  • Expert-level real-time performance tuning and profiling (perf, eBPF, ftrace/LTTng), and the judgment to know when to reach for each.

  • Experience with Git, Docker/OCI containers, and the CI/CD patterns appropriate for OS images and signed artifacts.

  • Fluency with the common buses and protocols of a sensor-heavy vehicle (USB, Ethernet/TSN, CAN, PCIe, SPI, I2C).

  • Experience with functional safety workflows (ISO 26262, MISRA, SOTIF) or a credible path to owning that work for us.

  • Experience with Nvidia Orin/Thor platforms and heterogeneous compute scheduling.

We Prefer

  • Prior work on autonomous vehicles, drones, or other safety-critical robotic systems.

  • Experience with ptp4l and TSN configurations in complex multi-sensor systems.

  • Experience with v4l2 / gstreamer.

  • Experience with QNX or other RTOS, and the judgment on when Linux is and isn't enough.

  • Deep knowledge of Linux/Yocto systems: package management, systemd, networking, and kernel configuration including PREEMPT_RT or equivalent real-time patches.

  • Familiarity with GPU acceleration frameworks (CUDA, Vulkan).

  • Open-source contributions to relevant projects (Linux kernel, ROS 2, DDS implementations, Yocto layers) or equivalent public technical work (talks, papers, standards participation).

  • Comfort working in a fast-paced startup environment and supporting field deployments at airports or other industrial sites.