fields.json

{
  "_comment": "Configure your application form fields here. Each field gets a dedicated editor with word counter, guidance callout, and status tracking. Edit this file — no code changes needed.",
  "fields": {
    "why_interested": {
      "label": "Why are you interested in this program?",
      "guidance": "Explain your motivation. Connect your background to the program goals. Show you understand what it offers and why you specifically belong here.",
      "reviewer_wants": "Authentic motivation, specific connection to the field, evidence you understand the domain.",
      "word_min": 150,
      "word_max": 300,
      "order": 1,
      "track": "both"
    },
    "relevant_background": {
      "label": "Describe your relevant background.",
      "guidance": "Summarize your hands-on experience relevant to this role. Be specific about what you built, researched, or discovered.",
      "reviewer_wants": "Concrete evidence of relevant work, technical depth, demonstrated capability.",
      "word_min": 75,
      "word_max": 150,
      "order": 2,
      "track": "both"
    },
    "excited_area": {
      "label": "What area are you most excited about?",
      "guidance": "Name a specific area and explain why it excites you. Reference concrete work or papers if possible.",
      "reviewer_wants": "Technical specificity, genuine enthusiasm, awareness of current frontiers.",
      "word_min": 75,
      "word_max": 150,
      "order": 3,
      "track": "both"
    },
    "code_samples": {
      "label": "Describe your code samples and what they demonstrate.",
      "guidance": "List 2-3 code samples. For each, explain what it does and what skill it demonstrates.",
      "reviewer_wants": "Clear descriptions, relevance to the role, code quality awareness.",
      "word_min": 100,
      "word_max": 200,
      "order": 4,
      "track": "both"
    },
    "commitment": {
      "label": "What is your availability and commitment level?",
      "guidance": "Be transparent about your availability. Disclose any other obligations.",
      "reviewer_wants": "Transparency, ability to commit sufficient time.",
      "word_min": 50,
      "word_max": 100,
      "order": 5,
      "track": "both"
    },
    "anything_else": {
      "label": "Anything else you want us to know?",
      "guidance": "Use this for anything that doesn't fit elsewhere. Unique perspectives, unconventional background, additional context.",
      "reviewer_wants": "Memorable differentiators, authenticity, things that make you stand out.",
      "word_min": 100,
      "word_max": 200,
      "order": 6,
      "track": "both"
    }
  },
  "cheatsheet": {
    "why_interested": {
      "what_to_hit": ["Personal journey from engineering to research", "The DataForge experience that revealed gaps in ML tooling", "Acme Corp's specific research on data quality and model evaluation"],
      "evidence_to_cite": ["evidence/career-profile.md (Narrative Arc section)", "evidence/project-portfolio.md (DataForge — relevance)", "evidence/platform-stats.md (Professional)"],
      "tips": ["Lead with the moment building DataForge when you realized better tooling needs research behind it", "Name Acme Corp's published work on evaluation frameworks — show you've done homework"]
    },
    "relevant_background": {
      "what_to_hit": ["6 years spanning full-stack, data, and ML infrastructure", "DataForge: 32K LOC production pipeline used by 3 teams", "Sentinel: real-time anomaly detection serving 2M events/day"],
      "evidence_to_cite": ["evidence/platform-stats.md (DataForge section)", "evidence/platform-stats.md (Sentinel section)", "evidence/project-portfolio.md (all projects)"],
      "tips": ["Lead with DataForge scale numbers — 32K LOC, 3 teams, 2.1M records/day", "Show the progression: built systems → saw gaps → want to research the gaps"]
    },
    "excited_area": {
      "what_to_hit": ["ML data quality and evaluation infrastructure", "Why current tools fail at scale (from DataForge experience)", "Specific research direction: automated data quality metrics"],
      "evidence_to_cite": ["evidence/project-portfolio.md (DataForge)", "evidence/career-profile.md (Narrative Arc)"],
      "tips": ["Pick ML infrastructure quality — it's your strongest angle", "Reference a specific pain point from DataForge that needs research, not just engineering"]
    },
    "code_samples": {
      "what_to_hit": ["DataForge pipeline engine (Rust): demonstrates systems thinking and performance optimization", "Sentinel anomaly detector (Python): demonstrates ML engineering and real-time inference", "scikit-learn PR: demonstrates open-source collaboration and research-grade code"],
      "evidence_to_cite": ["evidence/project-portfolio.md (DataForge — code samples)", "evidence/project-portfolio.md (Sentinel — code samples)", "evidence/platform-stats.md (Open Source)"],
      "tips": ["For each sample: what it does, what SKILL it demonstrates, why it matters for research engineering", "The scikit-learn PR shows you can work in a research-oriented codebase"]
    },
    "commitment": {
      "what_to_hit": ["Full-time availability starting September 2026", "Current role transition plan", "No competing obligations"],
      "evidence_to_cite": ["evidence/career-profile.md (Career Arc — current role end date)"],
      "tips": ["Brief and direct — state availability, confirm full-time commitment, mention transition timeline"]
    },
    "anything_else": {
      "what_to_hit": ["Open-source contributions to scikit-learn show research-community fit", "Self-taught Rust for DataForge performance layer — demonstrates learning drive", "Blog posts on ML infrastructure gaps bridge engineering and research communication"],
      "evidence_to_cite": ["evidence/project-portfolio.md (scikit-learn, DataForge Rust layer)", "evidence/platform-stats.md (Open Source section)"],
      "tips": ["Use this to show the researcher mindset hiding inside the engineer", "The blog posts are strong evidence you can communicate technical ideas clearly"]
    }
  }
}