Transparency

How MACH scoring works

MACH is how AION evaluates mission submissions—transparent, multi-dimensional, and built for European tech hiring teams who need defensible decisions, not black boxes.

The five dimensions

Each dimension is scored independently (0–20), then combined into a 0–100 total. This isolates strengths and weaknesses so hiring teams can compare candidates fairly.

Correctness

Does the work actually solve the problem? We look for runnable logic, sound assumptions, and alignment with the mission brief.

Craft

Is the solution well-structured and maintainable? Clear boundaries, sensible abstractions, and attention to edge cases matter here.

Reasoning

Are trade-offs explained honestly? Strong candidates show how they chose between alternatives—not just the happy path.

Communication

Can they articulate decisions for teammates and stakeholders? Clarity beats buzzwords; we reward readable explanations.

Originality

Is there a creative or non-obvious angle when appropriate? Pragmatic novelty scores higher than novelty for its own sake.

Methodology

  • Scored by Claude Sonnet 4.5 — a state-of-the-art model chosen for nuanced technical judgment across coding, prompting, and strategy missions.
  • Model + prompt version tracked — every score row stores the exact model ID and rubric prompt version so scores stay interpretable when we ship improvements.
  • Rubric calibrated for European tech roles — we bias toward clarity, maintainability, and GDPR-aware engineering norms—not Silicon Valley vanity metrics.

Trust signals

  • Every score is reproducible from stored model and prompt metadata.
  • Public score cards are tamper-evident by design (server-side computation; signed attestations planned).
  • GDPR-compliant evaluation flows—data minimization on what enters the model.
  • EU-hosted (Frankfurt) infrastructure for core application data.

MACH is AION's proprietary scoring methodology. It is not an acronym—think of it like FICO or ELO: a named standard you can trust over time.

Three score examples

Same MACH structure for every candidate: five dimensions (0–20 each), one total out of 100. Illustrative examples below — green, yellow, and red bands so you can read quality at a glance. Live public cards use the same dimension layout at /score/<submission_id>.

Example A — high score

92/100

Top 5%

Role

AI Engineer

Mission

RAG pipeline for legal docs

Correctness19/20

Production-ready code with proper error handling

Craft18/20

Clean abstraction, well-typed, modular

Reasoning18/20

Clearly explains chunking strategy + trade-offs

Communication18/20

Concise write-up, code comments where needed

Originality19/20

Hybrid retrieval approach, beyond standard tutorials

Illustrative example — not a real submission

Example B — medium score

76/100

Strong fit, may need mentoring

Role

ML Engineer

Mission

Customer churn prediction model

Correctness17/20

Model works, but feature engineering is basic

Craft15/20

Functional code, could be more modular

Reasoning16/20

Explains choice of XGBoost but skips validation strategy

Communication14/20

Writeup is brief, lacks business framing

Originality14/20

Standard approach, no novel insights

Illustrative example — not a real submission

Example C — low score

52/100

Below bar — would recommend skip

Role

Backend Engineer

Mission

API for user authentication

Correctness12/20

Works but has security vulnerabilities (token in URL)

Craft10/20

Tightly coupled, hard to test

Reasoning9/20

No explanation of design choices

Communication11/20

Minimal documentation

Originality10/20

Generic boilerplate solution

Illustrative example — not a real submission

MACH doesn't grade on a curve. We've scored submissions from 35 to 98. Only top performers reach 90+. This is why companies trust the score.

FAQ

What does “MACH” stand for?
Nothing—MACH is AION’s proprietary scoring methodology (like FICO or ELO). It is not an acronym; the name reflects our multi-dimensional evaluation lens.
Who runs the evaluation?
Each dimension is scored by Claude Sonnet 4.5 using a fixed rubric per mission type. Model version and prompt version are stored with every score for traceability.
Can companies tamper with scores?
Scores are computed server-side and persisted in our database. Public score cards expose only shareable fields—never raw submissions. Cryptographic attestation for third-party verification is on our roadmap.
Is candidate data sent to the model?
Only the mission brief and the candidate’s submitted write-up / links required for evaluation are used. Public cards never expose full submissions.
Where is data processed?
AION is built GDPR-first. Application data is hosted in the EU (Frankfurt). Anthropic API calls originate from our controlled scoring pipeline with audit logging.
How do I share a score with my hiring committee?
Each scored submission has a public score card URL (aionfeed.com/score/…). Share the link—no login required for viewers.