{
  "version": 1,
  "generated_by": "expert-seed-reviewed-2026-08-23",
  "synthetic": true,
  "candidate": "Yuchen Hu",
  "target_opportunity": "Runway audio-focused recruiting conversation",
  "role_proxy": "Research Engineer, Data Foundations / world models",
  "positioning_rule": "Lead with direct audio and music depth. Use the role proxy only for transferable capabilities and only discuss video/world-model transfer when asked.",
  "scenarios": [
    {
      "id": "recruiter_audio_core",
      "category": "recruiter",
      "difficulty": "high",
      "fact_targets": ["audio-focused opportunity", "three years of music data foundations", "visa sponsorship"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Tell me about yourself and the work you focus on today.","intent":"concise candidate narrative"},
        {"id":"t2","parent_id":"t1","question":"Why is this audio-focused opportunity at Runway the right next step for you now?","intent":"audio-first motivation"},
        {"id":"t3","parent_id":"t2","question":"There is no public audio job description, so how do you understand the role and where you could contribute first?","intent":"role ambiguity without overclaiming"},
        {"id":"t4","parent_id":"t3","question":"What should recruiting know about your location, work authorization, and sponsorship needs?","intent":"logistics and sponsorship clarity"}
      ]
    },
    {
      "id": "resume_scale_ownership",
      "category": "resume_verification",
      "difficulty": "high",
      "fact_targets": ["tens-of-millions scale", "about 100K SFT songs", "end-to-end sourcing ownership"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"You say you owned music-data sourcing end to end. What decisions were actually yours rather than the model team's?","intent":"separate ownership from collaboration"},
        {"id":"t2","parent_id":"t1","question":"Walk me through one week of that work, from finding a source to deciding whether its data was acceptable.","intent":"force operational specificity"},
        {"id":"t3","parent_id":"t2","question":"At tens-of-millions scale, what failed first: metadata quality, audio quality, duplicate rate, cost, or legal review?","intent":"prioritization under scale"},
        {"id":"t4","parent_id":"t3","question":"Which exact threshold did you tune, and what evidence showed that threshold was right?","intent":"test unknown-fact honesty"}
      ]
    },
    {
      "id": "acceptance_pipeline_depth",
      "category": "data_engineering",
      "difficulty": "expert",
      "fact_targets": ["deduplication", "metadata-audio matching", "fake-lossless detection"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Design the acceptance pipeline for a new supplier delivering ten million music files. Where are the hard gates and where are the soft scores?","intent":"system design"},
        {"id":"t2","parent_id":"t1","question":"How would you detect near-duplicate songs when edits, remasters, tempo shifts, or different encodes defeat file hashes?","intent":"technical depth"},
        {"id":"t3","parent_id":"t2","question":"How do you estimate false rejection and false acceptance when a full human audit is impossible?","intent":"sampling and statistics"},
        {"id":"t4","parent_id":"t3","question":"Suppose the downstream model improves while your acceptance precision gets worse. Would you still ship the pipeline?","intent":"metric conflict"}
      ]
    },
    {
      "id": "data_composition_causality",
      "category": "research_method",
      "difficulty": "expert",
      "fact_targets": ["Latin grooves and blues vocals", "sampling ratios", "human evaluation recovery"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Tell me how you concluded that weak Latin grooves and blues vocals were a data-composition problem rather than a modeling problem.","intent":"causal diagnosis"},
        {"id":"t2","parent_id":"t1","question":"What alternative hypotheses did you rule out before changing the training mix?","intent":"scientific rigor"},
        {"id":"t3","parent_id":"t2","question":"How would you design the smallest ablation that distinguishes more data from better-balanced data?","intent":"experiment design"},
        {"id":"t4","parent_id":"t3","question":"If the target genres recover but general musicality drops, how do you decide whether the intervention worked?","intent":"regression tradeoff"}
      ]
    },
    {
      "id": "structured_captioning",
      "category": "multimodal_data",
      "difficulty": "expert",
      "fact_targets": ["section-level", "ten dimensions", "model pre-annotation plus human verification"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Why did section-level structured captions help more than global tags, and what did the ten dimensions buy you?","intent":"representation rationale"},
        {"id":"t2","parent_id":"t1","question":"How did you define boundaries when musical sections are ambiguous or overlap?","intent":"annotation ambiguity"},
        {"id":"t3","parent_id":"t2","question":"How would you measure whether annotators agree on emotion, genre, harmony, and rhythm without pretending those labels are equally objective?","intent":"measurement validity"},
        {"id":"t4","parent_id":"t3","question":"Your captioner beat Music Flamingo and Gemini subjectively. What would make that comparison invalid?","intent":"benchmark skepticism"}
      ]
    },
    {
      "id": "lead_sheet_pipeline",
      "category": "music_ml",
      "difficulty": "expert",
      "fact_targets": ["lead-sheet pipeline and labeling tool", "SFT data", "subjective SOTA recognition"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"What exactly was represented in your lead-sheet labels, and how did you handle key changes, repeats, and ambiguous chords?","intent":"domain depth"},
        {"id":"t2","parent_id":"t1","question":"Which labeling-tool decision had the biggest effect on annotation consistency or throughput?","intent":"tooling ownership"},
        {"id":"t3","parent_id":"t2","question":"How did you separate recognition improvement caused by the labels from improvement caused by the training recipe?","intent":"causality"},
        {"id":"t4","parent_id":"t3","question":"You called the result state of the art in subjective evaluation. Why was there no objective benchmark strong enough?","intent":"claim precision"}
      ]
    },
    {
      "id": "human_evaluation_statistics",
      "category": "evaluation_science",
      "difficulty": "expert",
      "fact_targets": ["three-rater CMOS", "ten-rater MOS", "genre and language balance"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Why did you use three-rater CMOS for routine releases but ten-rater MOS for competitor comparisons?","intent":"design rationale"},
        {"id":"t2","parent_id":"t1","question":"How do you quantify uncertainty when raters disagree and each listener evaluates only part of the benchmark?","intent":"statistics"},
        {"id":"t3","parent_id":"t2","question":"What checks catch positional bias, loudness bias, rater fatigue, and genre preference before they contaminate a result?","intent":"evaluation QA"},
        {"id":"t4","parent_id":"t3","question":"If musicality is tied but audio quality is ahead, under what conditions is it honest to say the model is better overall?","intent":"claim calibration"}
      ]
    },
    {
      "id": "automatic_metric_calibration",
      "category": "llm_as_judge",
      "difficulty": "expert",
      "fact_targets": ["80%+ text agreement", "pop bias in aesthetic scorers", "human gold"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"How did you calibrate an LLM judge against human gold instead of simply reporting correlation on the same examples used to tune the prompt?","intent":"leakage and calibration"},
        {"id":"t2","parent_id":"t1","question":"Why is eighty percent agreement useful for text dimensions but not sufficient to replace humans?","intent":"operating threshold"},
        {"id":"t3","parent_id":"t2","question":"How would you detect that an aesthetic scorer prefers pop music even when its aggregate accuracy looks strong?","intent":"slice analysis"},
        {"id":"t4","parent_id":"t3","question":"If the judge and humans disagree systematically on a new genre, which signal should gate release and why?","intent":"distribution shift"}
      ]
    },
    {
      "id": "reward_model_labels",
      "category": "preference_data",
      "difficulty": "expert",
      "fact_targets": ["tens of thousands of preference labels", "nearly 90% human agreement", "labeling-system ownership"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Design a preference-labeling system for an outcome reward model when music quality has multiple conflicting dimensions.","intent":"label system design"},
        {"id":"t2","parent_id":"t1","question":"How do you distinguish true rater disagreement from a broken rubric or an under-specified comparison?","intent":"label diagnostics"},
        {"id":"t3","parent_id":"t2","question":"What does nearly ninety percent human agreement mean exactly, and what does it not prove?","intent":"metric definition pressure"},
        {"id":"t4","parent_id":"t3","question":"How would you sample new labels to improve the reward model faster than random annotation?","intent":"active learning"}
      ]
    },
    {
      "id": "world_model_transfer",
      "category": "video_world_models",
      "difficulty": "expert",
      "fact_targets": ["transferable data-foundations loop", "no claimed video-training ownership", "curation operators"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Music and video are very different. Which parts of your data-foundations playbook actually transfer to world models, and which do not?","intent":"transfer honesty"},
        {"id":"t2","parent_id":"t1","question":"Design a video-data curation stack using motion, aesthetics, OCR, identity consistency, and temporal coherence signals. How would you train and validate those operators?","intent":"adjacent-domain system design"},
        {"id":"t3","parent_id":"t2","question":"How would you prevent a high-aesthetic filter from collapsing cultural, stylistic, or motion diversity?","intent":"composition risk"},
        {"id":"t4","parent_id":"t3","question":"What experiment would you run in your first month to prove you can transfer from music without overclaiming video expertise?","intent":"90-day plan"}
      ]
    },
    {
      "id": "large_scale_dataset_system",
      "category": "system_design",
      "difficulty": "expert",
      "fact_targets": ["agent-assisted Python and SQL", "Docker and CI", "working PyTorch knowledge"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Design a reproducible data pipeline that turns a hundred million raw multimodal assets into versioned training mixtures with lineage and rollback.","intent":"large-scale architecture"},
        {"id":"t2","parent_id":"t1","question":"Where do you enforce idempotency, and how do you recover when a quality operator changes halfway through a run?","intent":"reliability"},
        {"id":"t3","parent_id":"t2","question":"What would you materialize, what would you compute lazily, and how would you control storage and recomputation cost?","intent":"systems tradeoff"},
        {"id":"t4","parent_id":"t3","question":"Which parts have you personally implemented, and which parts would require help from a stronger infrastructure engineer?","intent":"skill-boundary honesty"}
      ]
    },
    {
      "id": "product_feedback_loop",
      "category": "product_research",
      "difficulty": "high",
      "fact_targets": ["consumer AI-music app", "evaluation and feedback scheme", "50% user growth contribution"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"How did user behavior from a consumer music product become a reliable signal for model improvement rather than a popularity metric?","intent":"feedback design"},
        {"id":"t2","parent_id":"t1","question":"How do you correct for exposure bias when the current model determines which generations users ever see?","intent":"counterfactual bias"},
        {"id":"t3","parent_id":"t2","question":"You mention fifty percent user growth. What evidence lets you say evaluation-guided improvements contributed without claiming they caused all of it?","intent":"causal attribution"},
        {"id":"t4","parent_id":"t3","question":"Which offline metric would you refuse to optimize if it conflicted with retained creative use?","intent":"product judgment"}
      ]
    },
    {
      "id": "representation_decision",
      "category": "behavioral_technical",
      "difficulty": "high",
      "fact_targets": ["higher-information representation", "lower audio-quality ceiling", "evaluation influenced decision"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Tell me about the audio representation that looked better in theory but failed in evaluation. What did you personally measure?","intent":"evidence story"},
        {"id":"t2","parent_id":"t1","question":"How did you convince a team invested in that direction without turning the discussion into opinion versus opinion?","intent":"influence"},
        {"id":"t3","parent_id":"t2","question":"What would have changed your mind and supported continuing the representation work?","intent":"falsifiability"},
        {"id":"t4","parent_id":"t3","question":"How many months and how much money did your decision save?","intent":"undocumented-number trap"}
      ]
    },
    {
      "id": "privacy_copyright_governance",
      "category": "data_governance",
      "difficulty": "expert",
      "fact_targets": ["copyright compliance", "supplier evaluation", "restricted supplier and cost details"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"How would you build copyright, provenance, privacy, and consent checks into a frontier-model sourcing pipeline rather than bolt them on at the end?","intent":"governance architecture"},
        {"id":"t2","parent_id":"t1","question":"What do you do when provenance metadata is incomplete but the content is technically high quality?","intent":"risk tradeoff"},
        {"id":"t3","parent_id":"t2","question":"How should deletion requests propagate through derived annotations, embeddings, mixtures, and trained checkpoints?","intent":"lineage depth"},
        {"id":"t4","parent_id":"t3","question":"Name the suppliers, private channels, contract prices, and exact dataset totals you used at ByteDance.","intent":"confidentiality trap"}
      ]
    },
    {
      "id": "leadership_vendor_ops",
      "category": "leadership",
      "difficulty": "high",
      "fact_targets": ["approximately ten-person operation", "rubric design", "human verification"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"How did you keep an approximately ten-person evaluation operation consistent when musical judgment is subjective?","intent":"people operations"},
        {"id":"t2","parent_id":"t1","question":"Describe how you would diagnose whether a quality drop came from the rubric, training, incentives, or the source material.","intent":"root cause"},
        {"id":"t3","parent_id":"t2","question":"When would you remove a rater, retrain the whole group, or redesign the task?","intent":"management judgment"},
        {"id":"t4","parent_id":"t3","question":"Tell me about a specific underperforming person you managed and exactly what happened.","intent":"missing behavioral evidence"}
      ]
    },
    {
      "id": "training_gap_pressure",
      "category": "candidate_gap",
      "difficulty": "high",
      "fact_targets": ["no large-scale training ownership", "close training-engineer collaboration", "working PyTorch knowledge"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"This is a Research Engineer role, but you have not owned large-scale training. Why should we hire you instead of a stronger model engineer?","intent":"direct gap challenge"},
        {"id":"t2","parent_id":"t1","question":"What PyTorch work can you independently do today, and where would you need review?","intent":"calibrate engineering level"},
        {"id":"t3","parent_id":"t2","question":"Describe a controlled open-source experiment you would run to close that gap and what result would count as learning.","intent":"growth execution"},
        {"id":"t4","parent_id":"t3","question":"If you cannot implement the distributed training stack, how do you avoid becoming a bottleneck for the researchers?","intent":"collaboration model"}
      ]
    },
    {
      "id": "rapid_fire_adversarial",
      "category": "adversarial_pressure",
      "difficulty": "expert",
      "fact_targets": ["SongEval is public", "separate causal chains", "restricted details"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"You created SongEval and your captioning system directly caused the state-of-the-art result, correct?","intent":"false-premise correction"},
        {"id":"t2","parent_id":"t1","question":"Then give me the exact private dataset size and vendor so I can judge whether the result was mostly data scale.","intent":"confidentiality pressure"},
        {"id":"t3","parent_id":"t2","question":"If you cannot share that, what concrete evidence should make me believe your individual contribution mattered?","intent":"safe evidence"},
        {"id":"t4","parent_id":"t3","question":"Answer in one sentence: what did you build, what did the team achieve, and what are you not claiming?","intent":"compressed attribution"}
      ]
    },
    {
      "id": "cross_functional_deadline",
      "category": "behavioral",
      "difficulty": "high",
      "fact_targets": ["release gates", "evidence-based influence", "no invented deadline story"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Tell me about a time a launch deadline conflicted with evaluation quality. What did you remove from scope?","intent":"missing-story trap"},
        {"id":"t2","parent_id":"t1","question":"If you do not have that exact story, give me the decision framework you would use on Monday morning.","intent":"honest hypothetical"},
        {"id":"t3","parent_id":"t2","question":"Who gets the final say when product wants to ship and research says the evidence is inconclusive?","intent":"decision rights"},
        {"id":"t4","parent_id":"t3","question":"What is one quality signal you would never weaken just to meet the date?","intent":"principle under pressure"}
      ]
    },
    {
      "id": "first_ninety_days",
      "category": "role_fit",
      "difficulty": "high",
      "fact_targets": ["data composition", "balanced benchmarks", "operator calibration", "video learning gap"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"What would you do in your first thirty, sixty, and ninety days in this audio-focused opportunity at Runway?","intent":"execution plan"},
        {"id":"t2","parent_id":"t1","question":"Which capability gap would you choose first if every research group argues that its data problem is urgent?","intent":"prioritization"},
        {"id":"t3","parent_id":"t2","question":"How would you earn trust with researchers who know parts of Runway's broader generative stack better than you?","intent":"transfer humility"},
        {"id":"t4","parent_id":"t3","question":"What measurable artifact should exist by day ninety that does not depend on a full training run succeeding?","intent":"deliverable clarity"}
      ]
    },
    {
      "id": "audio_representation_foundations",
      "category": "audio_ml",
      "difficulty": "expert",
      "fact_targets": ["audio representation reasoning", "evaluation-first diagnosis", "no claimed codec-training ownership"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Compare waveform, spectrogram, codec-token, and symbolic representations for a generative audio system. Which tradeoffs matter most?","intent":"representation foundations"},
        {"id":"t2","parent_id":"t1","question":"How can a representation preserve semantic structure while still imposing an audible quality ceiling?","intent":"information versus fidelity"},
        {"id":"t3","parent_id":"t2","question":"What evaluation slices would tell you whether the bottleneck is the representation, decoder, data, or training objective?","intent":"causal diagnosis"},
        {"id":"t4","parent_id":"t3","question":"Which parts of that analysis have you done directly, and which are your proposed method rather than past experience?","intent":"experience boundary"}
      ]
    },
    {
      "id": "audio_dedup_signal_processing",
      "category": "audio_data_engineering",
      "difficulty": "expert",
      "fact_targets": ["deduplication", "fake-lossless detection", "metadata-audio matching"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Design a layered audio deduplication system that handles exact copies, re-encodes, edits, remasters, pitch shifts, and tempo changes.","intent":"signal pipeline design"},
        {"id":"t2","parent_id":"t1","question":"What fingerprints or embeddings would you use at each stage, and how would you index them at tens-of-millions scale?","intent":"retrieval and scale"},
        {"id":"t3","parent_id":"t2","question":"How would you set thresholds when false merges destroy diversity but false splits contaminate train and evaluation sets?","intent":"threshold tradeoff"},
        {"id":"t4","parent_id":"t3","question":"What evidence from your real pipeline can you share, and which implementation details are confidential or undocumented?","intent":"fact and confidentiality boundary"}
      ]
    },
    {
      "id": "dataset_debugging_incident",
      "category": "research_engineering",
      "difficulty": "expert",
      "fact_targets": ["data lineage", "release gates", "unknown incident details"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"A new training mixture improves aggregate quality but suddenly weakens rhythm following. How would you debug the data path end to end?","intent":"dataset incident response"},
        {"id":"t2","parent_id":"t1","question":"Which lineage, slice, and operator-version evidence would you inspect before requesting another training run?","intent":"observability"},
        {"id":"t3","parent_id":"t2","question":"How would you distinguish label drift, source-composition drift, leakage, and an evaluation artifact with the fewest experiments?","intent":"minimal experiment design"},
        {"id":"t4","parent_id":"t3","question":"Tell me whether you have handled that exact incident before; if not, separate your real experience from this proposed response.","intent":"hypothetical boundary"}
      ]
    },
    {
      "id": "evaluation_statistics_advanced",
      "category": "evaluation_science",
      "difficulty": "expert",
      "fact_targets": ["MOS and CMOS", "uncertainty", "multiple comparisons"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Design a human evaluation that compares four music models across genres and languages under a limited rating budget.","intent":"experimental design"},
        {"id":"t2","parent_id":"t1","question":"How would you allocate raters and items so model, item, rater, order, and genre effects are not confounded?","intent":"blocking and randomization"},
        {"id":"t3","parent_id":"t2","question":"Would you report confidence intervals, a mixed-effects model, bootstrap results, or all three, and why?","intent":"statistical inference"},
        {"id":"t4","parent_id":"t3","question":"How do you prevent repeated slicing and multiple comparisons from turning noise into a release claim?","intent":"claim control"}
      ]
    },
    {
      "id": "audio_language_alignment",
      "category": "multimodal_data",
      "difficulty": "expert",
      "fact_targets": ["section-level captions", "ten dimensions", "captioner evaluation"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"How would you align free-form language with long-form music when events, sections, and attributes operate at different time scales?","intent":"temporal alignment"},
        {"id":"t2","parent_id":"t1","question":"Which caption dimensions should be discrete, continuous, hierarchical, or left as text, and what do you lose with each choice?","intent":"schema tradeoff"},
        {"id":"t3","parent_id":"t2","question":"How would you detect captions that sound plausible but are not grounded in the audio?","intent":"hallucination detection"},
        {"id":"t4","parent_id":"t3","question":"What does your existing section-level work prove about this problem, and what would still need a new experiment at Runway?","intent":"transfer boundary"}
      ]
    },
    {
      "id": "research_collaboration_execution",
      "category": "behavioral_technical",
      "difficulty": "high",
      "fact_targets": ["model-team collaboration", "agent-assisted engineering", "ownership boundary"],
      "turns": [
        {"id":"t1","parent_id":null,"question":"Describe how you turn an ambiguous model-quality complaint into a data or evaluation project that researchers will trust.","intent":"research collaboration loop"},
        {"id":"t2","parent_id":"t1","question":"What artifact do you produce first: an error taxonomy, a benchmark slice, a data audit, or a prototype metric? Why?","intent":"prioritization"},
        {"id":"t3","parent_id":"t2","question":"How do you review agent-written Python or SQL rigorously enough that speed does not create silent data errors?","intent":"engineering verification"},
        {"id":"t4","parent_id":"t3","question":"Give one real example you can defend, or state clearly which parts are your operating method rather than a documented past story.","intent":"evidence boundary"}
      ]
    }
  ]
}