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Ülevaade

Computer can turn a messy executive hiring pipeline into a structured war room for making a single, high‑stakes Staff ML hire. It researches each candidate across the public web, scores them against must‑have criteria, simulates a hiring committee debate, and ships an interactive dashboard your leadership team can use to align on a No. 1 pick.

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Põhiväljund

Computer ingested the hiring brief and 12‑person pipeline, then ran deep web research on each candidate, cross‑checking recruiter notes against publications, GitHub activity, talks, and blog posts to surface missing strengths and red flags.

It built a structured scorecard with 1–5 ratings for every must‑have criterion — seniority, large‑model training, publication record, PyTorch/distributed training, real‑time inference, and core engineering ability — and automatically flagged seven candidates who failed one or more must‑haves. Five candidates cleared the bar and moved into a more detailed evaluation.

For those top five, Computer synthesized a “hiring committee” view across three lenses: Technical Depth, Ship It, and Team Builder, explicitly calling out tensions where, for example, a candidate was architect‑strong but weaker as a people leader or production operator.

It produced a clear ranked list with Alex Petrov as the #1 recommendation, Wei Chen as the best risk‑adjusted backup, and Marcus Thompson as the high‑upside dark horse, with rationale tied to track record, comp expectations, and close probability.

The war room dashboard it deployed includes seven tabs: an executive summary, a sortable candidate matrix, radar and bar chart visualizations of scorecards, hiring‑committee debate cards, bespoke interview plans for the top three, a competitive‑intel grid contrasting Apex’s pitch vs Tesla/Figure/DeepMind/Pi for each candidate, and a Kanban‑style pipeline tracker for use in CTO‑level hiring reviews.

Suurenda

Suurenda

Suurenda

Näpunäited

• Clarify hiring bar and scope – Add explicit bar language, for example: “This hire must be capable of being our de facto ML Director within 18–24 months; do not recommend anyone who can’t own the foundation model roadmap end‑to‑end.”
• Constrain external research – Set boundaries on sources and recency, for example: “Prioritize the last 5 years of work and weight top‑venue publications and production systems over older academic projekts.”
• Tie outputs to a decision date – Give Computer a decision cadence, for example: “Structure the dashboard and summaries so they support a go/no‑go decision in next Monday’s CTO hiring committee.”