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poorvith-mp/skills-personal/job-search
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PublishedOctober 5, 2026 at 05:58 AM
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version: "1.0.0" name: job-search last_reviewed: 2026-09-06 group: Career description: >- Run the application pipeline: target list, stage tracking, follow-up cadence and a weekly read on where applications die. Use when planning job search pipelines, target companies, or warm outreach.


job-search

Core Philosophy

Submitting 400 blind applications through LinkedIn "Easy Apply" is a statistical dead end that yields a $< 1\%$ interview rate and massive emotional burnout. A professional technical job search must be managed like an enterprise B2B sales pipeline: strict target account qualification, reverse-engineering company engineering stacks, activating warm 2nd-degree referrals, and demonstrating proof-of-work through custom technical artifacts before the first interview.


4-Step Technical Job Search Pipeline

Step 1: Target Account Curation & Tiering (30 Accounts)

  1. Tiered Account Architecture:
  • Tier 1 (Top 5 - Dream Accounts): Uncompromising fit, high compensation, exceptional culture. Every application gets customized work samples and multi-threaded warm outreach.
  • Tier 2 (15 Accounts - Strong Fits): High alignment with current skills, growing engineering organizations, Series B through Pre-IPO. Standard tailored resume + warm intro.
  • Tier 3 (10 Accounts - Benchmark/Safety): Solid roles for pipeline velocity, interview practice, and establishing market compensation baselines.
  1. Qualification Filters:
  • Tech stack match ($\ge 75\%$ overlap with your strengths).
  • Financial runway (profitable or raised within last 12 months).
  • Engineering leadership pedigree and low Glassdoor/Levels.fyi red flags.

Step 2: Proof-of-Work Artifact Engineering

  1. The Pre-Interview Work Sample:
  • For Tier 1 accounts, never submit just a PDF resume. Provide a tangible technical artifact:
  • Audit their public GitHub repositories and open a well-crafted PR or detailed issue.
  • Build a 1-page technical memo analyzing their API architecture or performance bottlenecks.
  • Record a 2-minute Loom demonstrating a solution to a problem their job description mentions.

Step 3: Warm Referral & Multi-Threading Playbook

  1. Network Triangulation:
  • Never apply cold on the careers page if a 2nd-degree connection exists.
  • Map connections: Search LinkedIn for engineering alumni from your previous companies or universities working at the target account.
  1. The 3-Touch Referral Outreach (Under 100 Words):
  • Message to Peer Engineer:
  • "Hey [Name], saw your team at [Company] is scaling the distributed data team. Loved your recent post on [Topic]. I've spent the last 3 years optimizing ClickHouse clusters at [Previous Org]. Would love to ask 2 quick questions about how your team handles schema migrations before I apply formally. No pressure at all!"
  1. Direct Hiring Manager Outreach:
  • Identify the Director/VP of Engineering or Engineering Manager owning the headcount. Send a concise email highlighting your 3 core relevant wins and attaching your custom technical artifact.

Step 4: Pipeline CRM Tracking & Conversion Diagnostics

  1. Pipeline Stage Metrics:
  • Prospecting: Target identified, contact mapped.
  • Outreach Active: Message sent, awaiting referral response.
  • Applied / Referred: Formal application submitted with internal tag.
  • Recruiter Screen: 30-minute culture and background alignment.
  • Technical Screen / Take-Home: Coding or system design round.
  • Onsite Loop: Multi-round executive and peer panel.
  • Offer Stage: Negotiation and closing.
  1. Funnel Health Diagnostics:
  • If Recruiter Screen conversion $< 40\%$: Resume bullets lack quantified impact or tech keywords.
  • If Technical Screen conversion $< 50\%$: Brush up on DSA or distributed systems fundamentals.
  • If Onsite-to-Offer conversion $< 33\%$: Behavioral answers (STAR method) or culture fit signals need calibration.

Deliverable Format: Job Search Pipeline Spec (JOB-SEARCH-PIPELINE.md)

markdown
# Technical Job Search Pipeline & CRM
## 1. Target Criteria & Constraints
-**Target Role**: [e.g. Senior Staff Backend Engineer]
-**Target Comp Band**: [$X - $Y base / total package]
-**Location / Mode**: [Remote / Hybrid / City]
-**Core Tech Stack**: [Go, Rust, PostgreSQL, Kubernetes]
## 2. Tier 1 Target Account Matrix
| Company | Team / Division | Engineering Manager / Recruiter | Warm Angle / Connection | Proof-of-Work Artifact | Status |
|---|---|---|---|---|---|
| [Target A] | Core Infra | [Name/Profile] | Ex-colleague at company | Audited open source CLI | Referral requested |
| [Target B] | Platform | [Name/Profile] | Met at GopherCon | 1-page migration memo | Recruiter screen scheduled |
## 3. Funnel Velocity Dashboard
-**Total Accounts Mapped**: [Count]
-**Active Referrals / Conversations**: [Count]
-**Technical Screens Active**: [Count]
-**Onsite Loops Scheduled**: [Count]
-**Offers In Hand**: [Count]

Worked Example: Senior Infrastructure Engineer Transition

  • Strategy: Bypassed cold job boards. Built a target list of 25 companies using Kubernetes in production.
  • Proof-of-Work: Contributed a bug fix to the target company's open-source Terraform provider before reaching out.
  • Outreach: Messaged the author of the pull request on LinkedIn with a 3-sentence note referencing the fix.
  • Outcome: Secured direct VP of Engineering interview within 48 hours; received offer with 25% compensation increase in 3 weeks.

Verification Checklist

  • [ ] Target account list is capped at 30 high-conviction companies categorized into Tiers 1–3.
  • [ ] Cold applications are prohibited for Tier 1 targets; warm intro or custom artifact is mandatory.
  • [ ] Outreach messages are strictly $< 100$ words with zero generic flattery.
  • [ ] Weekly funnel stage conversion rates are calculated to diagnose drop-offs.
  • [ ] Salary expectations and minimum walk-away numbers are documented prior to initial screens.

Anti-Patterns

  • Volume Spam: Spraying 50 resumes a day to random job posts without customizing a single bullet point.
  • Passive Waiting: Waiting for recruiters to discover an unoptimized LinkedIn profile.
  • Skipping the Hiring Manager: Relying solely on automated portal submissions rather than finding the engineering leader owning the role.
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