Discussion: Job seekers can't find a job and Employers can't find an employees

Hacker News Discussion ↗

Engineers describe a tech job market where thousands of applicants vie for each role, automated filters and LeetCode-style interviews dominate, and networking often matters more than formal applications. Employers, for their part, complain about resume spam, fake or inflated credentials, and high training costs, leading them to chase “perfect” candidates with very specific skills instead of investing in juniors or retraining. Commenters frame this as a market failure driven by information asymmetry, risk aversion, and misaligned incentives, and propose fixes ranging from simpler, faster hiring and more on-the-job training to standardized assessments, unions, and new matching platforms.

State of the Tech Job Market

  • Many report a sharply worse market than 2020–2022: higher applicant volumes per role, fewer callbacks, more ghosting, more fake or “evergreen” postings.
  • New grads and juniors are hit hardest; entry-level roles are disappearing or massively oversubscribed.
  • Senior people with strong resumes also describe 6–18+ months of unemployment or underemployment.
  • Some see this as a classic “market for lemons”: too many low‑signal resumes and fake ads, so everyone over-filters and good matches fail to connect.

Networking and the Hidden Job Market

  • Strong consensus that knowing hiring managers or insiders is the single biggest advantage.
  • Referrals are seen as a way around resume spam, keyword games, and broken ATS filters.
  • This favors well‑connected, higher‑status candidates and can disadvantage people from weaker networks or minority/low‑SES backgrounds.

Hiring Practices and Interviews

  • Many describe processes as slow, opaque, and heavily optimized to avoid “false positives,” leading to huge numbers of “false negatives.”
  • Widespread criticism of:
    • Leetcode/algorithm screens, especially time‑boxed or puzzle‑like ones.
    • Long multi‑round pipelines, take‑homes, and arbitrary trivia.
    • HR/recruiter filters that don’t understand the work.
  • Some hiring managers defend basic coding or aptitude tests as necessary to filter out large numbers of truly unqualified applicants.
  • Others report success hiring via “smart and gets things done,” portfolio/code review, and conversational technical interviews instead.

Skills, Training, and Structural Mismatch

  • One camp attributes the paradox to structural unemployment: rapid shifts (ML/LLMs, specific stacks) and employers refusing to train.
  • Another argues employers over‑index on hyper‑specific stacks and “top 10%” candidates even for mundane roles.
  • There is tension between:
    • Hiring only people already trained (low risk, high bar).
    • Hiring for potential and investing in training (higher risk, higher long‑term payoff).

Recruiters, Platforms, and Volume Problems

  • Recruiters are viewed as a mixed bag: a few high‑quality ones are invaluable; many are spammy, domain‑ignorant, and incentive‑misaligned.
  • Job boards/LinkedIn/ATS are seen as:
    • Flooded with low‑effort, AI‑generated, or keyword‑stuffed applications.
    • Incentivized to maximize volume, not match quality.
  • Some note that remote work and global candidates massively increased applicant counts per posting, worsening all of the above.

Proposed Remedies and Experiments

  • Ideas raised: “Tinder for jobs”–style matching, standardized tests or certifications, unions or professional guilds, more transparent salaries, shorter/faster hiring loops, “hire fast, fire fast,” better entry‑level training, and high‑trust environments.
  • No consensus on a silver bullet; most see it as a hard matching problem with misaligned incentives on all sides.