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Sample · Expert candidate
Readiness report

Dr. Maya Chen

Senior LLM Evaluator · Former NLP Researcher

Top-decile profile: published methodology, runs evaluation pods, owns red-teaming output that has shipped to model trainers. Premium vendors will compete for this candidate.

Seattle, WA (Remote)9 years (4 in LLM evaluation)
92
Overall
readiness score
out of 100
Screening odds
92%
Interview odds
71%
Offer odds
38%
Readiness across 10 categories
How this candidate scores on the dimensions every AI workforce vendor cares about.
Communication Skills95/100

Authored evaluator-onboarding docs adopted by 60+ contractors.

Analytical Thinking94/100

Designs rubrics and reasons about failure modes statistically.

Technical Skills91/100

Python, evals harness, light fine-tuning experience.

Research Ability96/100

Published two papers and a methodology blog post.

Writing Ability92/100

Clear, structured prose at researcher level.

Domain Expertise93/100

Deep NLP + safety domain expertise.

Professionalism96/100

Consistent multi-year contracts with renewals.

Remote Work Readiness94/100

9 years fully remote across 3 timezones.

AI Evaluation Readiness95/100

Owns evaluation strategy end-to-end.

Data Annotation Readiness90/100

Trained and audited 9-person grader pods.

Company fit
Top 6 vendors with verdict and per-company readiness score.
Scale AI
Top fit — likely fast-tracked
94
Outlier
Senior pod lead role
92
DataAnnotation
Premium tier
90
Surge AI
Methodology lead candidate
91
Turing
Strong, slight stack gap
84
Mindrift
Senior evaluator
88
Strengths
  • · Published evaluation methodology cited internally by two foundation-model labs.
  • · Owns red-teaming output that shipped into a 70B model training cycle.
  • · Trains and audits 9-person grader pods with documented quality lift.
Weaknesses
  • · Compensation expectations may exceed entry-tier vendor budgets.
  • · Light on multilingual annotation — limits a few localization-heavy contracts.
  • · Resume is dense; some recruiters skim and miss the published artifacts.
Recommended next steps
Projected overall score after this roadmap: 96/100
  1. 1Add a 3-line 'highlights' band at the top so skimmers see the published work.
  2. 2Filter applications to senior / lead / staff roles only — skip microtask vendors.
  3. 3Negotiate via referral + portfolio link; do not apply through cold portals.
  4. 4Optional: add a multilingual QA bullet from past contract to widen the funnel.
Missing keywords recruiters scan for
Multilingual QA
Localization
Annotation throughput SLA

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