legacy/Quick_Ref_Checklist.md

# Quick Reference: 6-Month Parallel Execution Checklist

## CURRENT STATUS (November 7, 2025)

**What You Have:**
- ✅ Master's degree in Signal Processing
- ✅ Published speech AI projects (SAD, SID, ASR)
- ✅ Thesis on deep learning (electromagnetic scattering)
- ✅ RTX 5060 Ti 16GB GPU
- ✅ 35+ hours/week available
- ✅ Located in Germany (major advantage)

**Your Target:**
- Job offer from voice AI company in Germany within 6 months
- Companies: ElevenLabs, Parloa, voize, audEERING, ai|coustics (primary)
- Roles: ML Engineer + Speech/Audio AI Engineer (hybrid)
- Remote/Hybrid/On-site: Flexible

---

## MONTH 1-2: PORTFOLIO TIER 1 (November - December 2025)

### Project 1: Whisper ASR Fine-tuning (Weeks 1-6)
```
Week 1-2: Setup + Data prep
  - Create conda environment (PyTorch 2.0, CUDA 12.5)
  - Download Common Voice German (~40 hours)
  - Implement data loading pipeline
  
Week 3-4: Fine-tuning
  - Fine-tune Whisper-small on German data
  - Use mixed precision (FP16) + gradient checkpointing
  - Expected: 15% WER improvement
  
Week 5: Evaluation & Optimization
  - Calculate WER/CER metrics
  - Compare to baseline
  - Optimize inference latency
  
Week 6: Deployment
  - Deploy to Hugging Face Spaces (free)
  - Create REST API with FastAPI
  - Push to GitHub with full documentation
```

**Deliverables:**
- [ ] GitHub repo: `whisper-german-asr`
- [ ] Hugging Face Space with live demo
- [ ] README with benchmarks and usage
- [ ] Blog post: "Fine-tuning Whisper for German ASR"

---

### Project 2: Real-Time VAD + Speaker Diarization (Weeks 1-6 parallel)
```
Week 1-2: VAD System (Silero VAD)
  - Implement Silero Voice Activity Detection
  - Test on various audio conditions
  - Measure latency (<100ms target)
  
Week 3-4: Speaker Diarization (Pyannote)
  - Set up Pyannote.audio pipeline
  - Test on multi-speaker scenarios
  - Measure DER (Diarization Error Rate)
  
Week 5: Integration
  - Combine VAD + Diarization
  - Build end-to-end pipeline
  - Real-time streaming support
  
Week 6: Deployment
  - Containerize with Docker
  - Deploy to Hugging Face Spaces
  - Create Gradio interface
```

**Deliverables:**
- [ ] GitHub repo: `realtime-speaker-diarization`
- [ ] Gradio demo with streaming audio
- [ ] Docker image for deployment
- [ ] Benchmarks on FEARLESS STEPS data (reference your existing project)

---

### Project 3: Speech Emotion Recognition (Weeks 1-6 parallel)
```
Week 1-2: Dataset prep (RAVDESS)
  - Download RAVDESS emotion dataset (1400 files)
  - Extract mel-spectrograms + MFCCs
  - Create train/val/test splits
  
Week 3-4: Model training
  - Build CNN architecture
  - Train on emotion classification (8 classes)
  - Target: 75%+ accuracy
  
Week 5: Evaluation & visualization
  - Confusion matrix
  - Class-wise metrics
  - Attention visualization
  
Week 6: Demo & deployment
  - Streamlit app for real-time demo
  - Deploy to Streamlit Cloud (free)
  - Upload to Hugging Face Model Hub
```

**Deliverables:**
- [ ] GitHub repo: `speech-emotion-recognition`
- [ ] Live Streamlit demo
- [ ] Trained model on Hugging Face
- [ ] Blog post: "Building Emotion Recognition from Speech"

---

### Supporting Tasks (Weeks 1-8)
- [ ] Create professional portfolio website (GitHub Pages)
- [ ] Write 2 technical blog posts (Medium/Dev.to)
- [ ] Update LinkedIn profile with project links
- [ ] Set up GitHub profile (pin 6 best repos)
- [ ] Create Hugging Face account and upload models

---

## PORTFOLIO SHOWCASE CHECKLIST (End of Month 2)

**GitHub:**
- [ ] 3 repositories with comprehensive READMEs
- [ ] Each with: requirements.txt, Dockerfile, model cards
- [ ] Code is clean, documented, well-structured
- [ ] At least 50 stars total (organic growth OK)

**Blog:**
- [ ] 2-3 posts on Medium/Dev.to with code examples
- [ ] 500+ words each
- [ ] Include: problem statement, architecture, results, lessons learned

**Deployed Demos:**
- [ ] Project 1: Live Whisper demo (Hugging Face Spaces)
- [ ] Project 2: Diarization demo with streaming (Gradio)
- [ ] Project 3: Emotion detection demo (Streamlit)

**Portfolio Website:**
- [ ] Professional design (minimal, clean)
- [ ] Project descriptions with links to code + demos
- [ ] About section (story + skills)
- [ ] Contact information
- [ ] Mobile-responsive

---

## MONTH 2-3: ACTIVE JOB SEARCH PHASE

### Application Wave 1: Tier 1 Companies (December)

**Target Companies:** 5 companies
1. ElevenLabs (London + Remote)
2. Parloa (Berlin)
3. voize (Berlin)
4. audEERING (Munich)
5. ai|coustics (Berlin)

**For Each Company:**
- [ ] Research: Learn about company, products, team
- [ ] Customize: Tailor resume + cover letter (100%)
- [ ] Personal touch: Reference specific projects or team members
- [ ] Application: Submit through official channels + follow up

**Effort:** 10 hours per application (5 × 10 = 50 hours total)

**Expected Outcome:**
- 0-1 first-round interviews (not guaranteed, but possible)
- Feedback/rejections (valuable for iteration)

---

### LinkedIn Outreach Strategy (December)

**Goal:** Connect with 10 engineers at target companies

**Process:**
1. Find engineers on LinkedIn (search: "ElevenLabs" + "Engineer")
2. Personalized message (NOT generic):
   ```
   "Hi [Name], I was impressed by your work on [specific project/achievement].
   I'm building voice AI projects (multilingual ASR, speaker diarization) and
   would love to learn about your experience at ElevenLabs. Would you have 15
   minutes for a chat?"
   ```
3. Wait 2-3 days before follow-up
4. **Offer value:** Share your project or article, not just asking for help

**Expected Response Rate:** 10-20% (1-2 connections)

---

## MONTH 3-4: PORTFOLIO TIER 2 + APPLICATIONS

### Project 4: Text-to-Speech with Voice Cloning (Weeks 9-12)

**Quick Timeline (because Tier 1 is already strong):**
- [ ] Week 9: Setup Coqui TTS framework
- [ ] Week 10: Voice encoding + few-shot adaptation
- [ ] Week 11: Multi-speaker TTS system
- [ ] Week 12: Deploy + create demo

**Deliverables:**
- [ ] GitHub repo: `voice-cloning-tts`
- [ ] Live demo (try 3-5 different voices)
- [ ] Blog post: "Voice Cloning at Home: Technical Deep Dive"

---

### Project 5: Voice-Based Chatbot (Weeks 13-16 start)

**High-level architecture:**
```
User Voice Input
    ↓
[ASR] (Whisper)
    ↓
[NLU] (Intent recognition)
    ↓
[LLM] (GPT-4 / Open LLM)
    ↓
[TTS] (Coqui / ElevenLabs API)
    ↓
Voice Output
```

**Timeline:**
- [ ] Week 13-14: Integrate ASR + TTS + LLM
- [ ] Week 15: Test + optimize latency
- [ ] Week 16: Deploy (API + web interface)

---

### Application Wave 2: Tier 2 Companies (January-February)

**Target Companies:** 10-15 companies
- Cerence (automotive)
- Continental R&D (automotive)
- Synthflow AI (Berlin)
- Deutsche Telekom AI Lab
- SAP AI Research
- German tech consulting firms

**Strategy:**
- 60-80% customization (template base, customize key sections)
- Leverage network: Ask LinkedIn connections for referrals
- Direct outreach: Email hiring managers directly (find on LinkedIn)

**Volume:** 3-4 applications per week

---

## MONTH 4-5: INTERVIEW PREPARATION

### LeetCode & Coding Interview (Weeks 17-20)

**Target:** 50 problems, all categories

**Weekly breakdown:**
- 10 problems/week (3 hours)
- Focus: Arrays, Strings, Trees, Graphs, DP
- Difficulty: 60% Easy, 30% Medium, 10% Hard
- Platform: LeetCode, HackerRank

**Resources:**
- Blind 75 (optimized problem list)
- Neetcode.io (video explanations)
- Grind 75 (extended version)

---

### ML System Design (Weeks 17-20)

**Practice scenarios (prepare for each):**

1. **"Design an ASR system at scale"**
   - Problem statement: Real-time speech → text
   - Architecture: Frontend (audio capture) → ASR model → Backend
   - Challenges: Latency, accuracy, scalability
   - Your answer: Walk through Whisper fine-tuning approach

2. **"Design a voice cloning system"**
   - Problem: Few-shot voice adaptation
   - Approach: Speaker embeddings + TTS
   - Trade-offs: Quality vs. latency

3. **"Design a speaker diarization system"**
   - Problem: Identify who spoke when
   - Your project: Diarization using Pyannote

**Practice:** Do 1 mock interview per week (use Pramp or interviewing.io)

---

### Behavioral Interview Prep

**Your STAR Stories (prepare 5):**

1. **Challenge & Solution Story**
   - Story: "My Master's thesis involved solving inverse EM problems with deep learning"
   - Challenge: Massive computational cost, data generation difficulty
   - Action: Used synthetic data + U-Net + optimization techniques
   - Result: 4000x speedup

2. **Collaboration Story**
   - Story: "FEARLESS STEPS project with 5 teammates"
   - Challenge: Coordinating complex pipeline (SAD → SID → ASR)
   - Action: Clear communication, documentation, regular syncs
   - Result: Published paper, successful deployment

3. **Learning & Growth Story**
   - Story: "Learned deployment best practices while building portfolio"
   - Challenge: Limited resources (RTX 5060 Ti)
   - Action: Optimization techniques (mixed precision, quantization)
   - Result: Deployed 3 models to production on free platforms

4. **Conflict Resolution Story**
   - Story: "Debugged production issue in speech processing pipeline"
   - Challenge: Model was producing random outputs
   - Action: Systematic debugging, data validation
   - Result: Fixed data preprocessing issue, improved robustness

5. **Impact Story**
   - Story: "Building portfolio projects to enter AI industry"
   - Challenge: Competitive market, need to stand out
   - Action: Built 5 production-ready projects, deployed, documented
   - Result: Getting interviews, building professional reputation

---

### Mock Interview Schedule (Weeks 17-24)

- Week 17-18: 2 coding interviews (LeetCode-style)
- Week 19-20: 2 system design interviews
- Week 21-22: 2 behavioral interviews
- Week 23-24: 2 full interview simulations (all 3 rounds)

**Resources:**
- Pramp (free mock interviews)
- Interviewing.io
- Interview Kickstart (paid, but high quality)

---

## MONTH 5-6: FINAL PHASE & OFFERS

### Application Wave 3: Tier 3 + Final Push (March-April)

**Target:** 20-30 applications to smaller companies, startups, consultancies

**Strategy:**
- 30-50% customization (mostly templates)
- Focus on volume
- Target: 1-2 offers

**Companies:**
- YC-backed startups (AngelList.com)
- Tech consulting (Accenture, Deloitte AI practices)
- Corporate R&D labs (Siemens, Bosch, Volkswagen)
- Growth-stage companies on Crunchbase

---

### Interview Pipeline Management

**Track everything in spreadsheet:**

| Company | Position | Date Applied | Status | Interview 1 | Interview 2 | Status | Notes |
|---------|----------|--------------|--------|-----------|-----------|--------|-------|
| ElevenLabs | ML Engineer | Dec 15 | Submitted | Jan 5 | Jan 15 | Passed R2 | Waiting for R3 |
| Parloa | ASR Engineer | Dec 20 | Submitted | - | - | Rejected | Good learning |
| voize | ML Eng | Jan 5 | Submitted | Jan 20 | - | Pending R2 | Good fit |

**Weekly review:**
- [ ] How many first-round interviews?
- [ ] What's the response rate? (should be 5-10%)
- [ ] Are rejections pattern-based?
- [ ] Adjust strategy if needed

---

### Offer Negotiation

**When you get an offer:**
1. **Don't accept immediately**
   - "Thank you! I'm very excited. Can I think about it for 2-3 days?"

2. **Understand the offer:**
   - Base salary
   - Bonus structure (if any)
   - Benefits (health insurance, vacation, home office)
   - Stock options (if startup)
   - Remote policy
   - Budget for learning/conferences

3. **Research market rate:**
   - German salary: €50,000-80,000 for ML Engineer (depending on experience)
   - Add 10-20% premium for startups (equity trade-off)
   - Compare on Glassdoor, Levels.fyi

4. **Negotiate:**
   - "I'm very interested in this role. Based on my experience and market research, I was hoping for X salary. Would that be possible?"
   - Negotiate everything: salary, remote flexibility, learning budget, vacation days

5. **Get everything in writing:**
   - Before resigning from any current role

---

## WEEKLY RHYTHM TEMPLATE

### Monday
- [ ] Review previous week's progress
- [ ] Plan week ahead (5 key tasks)
- [ ] Check applications status (new responses?)
- [ ] 2-3 hours: Project development

### Tuesday-Thursday
- [ ] 5 hours/day: Project development (main work)
- [ ] 1 hour/day: Learning (courses, papers)
- [ ] 30 min/day: LeetCode or system design
- [ ] 30 min/day: LinkedIn engagement (comment, share, connect)

### Friday
- [ ] 3 hours: Project optimization/deployment
- [ ] 1 hour: Blog writing or documentation
- [ ] 1 hour: Applications + outreach (if in active phase)

### Saturday
- [ ] 4-6 hours: Deep work on complex project
- [ ] 1-2 hours: Open-source contributions
- [ ] 1 hour: Content creation (record video, write article)

### Sunday
- [ ] 2-3 hours: Interview prep (LeetCode, system design, mock interviews)
- [ ] 1-2 hours: Planning for next week
- [ ] 1-2 hours: Optional blogging/content

---

## SUCCESS INDICATORS BY MONTH

### Month 2 (End of December 2025)
- [ ] 3 projects deployed and working
- [ ] Portfolio website live
- [ ] 2 blog posts published
- [ ] 5 applications sent
- [ ] 10 LinkedIn connections to target companies
- [ ] 0-1 interview requests (bonus)

**Status Check:** Are projects working? Is portfolio visible? Is anything preventing applications?

### Month 3 (End of January 2026)
- [ ] Projects 1-3 polished and showcased
- [ ] 20 applications sent total
- [ ] 1-3 first-round interviews
- [ ] 3-5 LinkedIn conversations
- [ ] 3 blog posts published

**Status Check:** Getting any response? If not, something is wrong. Debug immediately.

### Month 4 (End of February 2026)
- [ ] Projects 4-5 started/deployed
- [ ] 30 applications sent total
- [ ] 3-5 first-round interviews
- [ ] 1-2 second-round interviews
- [ ] 30+ LeetCode problems completed
- [ ] 4+ mock interviews done

**Status Check:** Should have at least 1-2 companies seriously interested.

### Month 5 (End of March 2026)
- [ ] All projects completed
- [ ] 40-50 applications sent
- [ ] 5+ interviews at various stages
- [ ] 2-3 offer conversations
- [ ] LeetCode: 50 problems
- [ ] Mock interviews: 8+ sessions

**Status Check:** Should be in final rounds with 1-2 companies.

### Month 6 (End of April 2026)
- [ ] Offers received from 1-2 companies
- [ ] Negotiating terms
- [ ] Preparing for first day
- [ ] Celebrating! 🎉

---

## RED FLAGS & COURSE CORRECTIONS

### "I'm not getting any responses after 2 weeks"
- [ ] Check ATS compatibility of resume
- [ ] Get resume reviewed by someone
- [ ] Verify cover letters are customized
- [ ] Make sure portfolio is visible
- [ ] Try direct outreach instead of job board portals

### "I'm getting rejections but no interviews"
- [ ] Problem: Resume/portfolio not matching role requirements
- [ ] Solution: 
  - Emphasize specific tech stack company uses
  - Highlight most relevant projects first
  - Customize cover letter more

### "I'm getting interviews but no offers"
- [ ] Problem: Failing technical or behavioral interview
- [ ] Solution:
  - Record yourself doing mock interviews
  - Get feedback from mentors
  - Focus weak area intensively
  - Practice more (LeetCode, system design)

### "Projects are taking too long"
- [ ] Solution: Ship MVP version first, polish later
- [ ] Focus on "good enough to deploy" not "perfect code"
- [ ] Reduce scope (3 excellent > 6 mediocre)
- [ ] Use existing models/frameworks (don't build from scratch)

---

## ESSENTIAL RESOURCES

### Code Repositories (Bookmark these)
- HuggingFace Transformers: https://github.com/huggingface/transformers
- Pyannote.audio: https://github.com/pyannote/pyannote-audio
- Silero VAD: https://github.com/snakers4/silero-vad
- Coqui TTS: https://github.com/coqui-ai/TTS

### Learning (Free)
- HuggingFace Audio Course: https://huggingface.co/course
- Made with ML (ML systems): https://madewithml.com/
- Papers with Code (speech): https://paperswithcode.com/

### Job Search
- AngelList Talent: https://wellfound.com/
- German Tech Jobs: https://germantechjobs.de/
- LinkedIn Jobs: https://www.linkedin.com/jobs/

### Applications
- Hugging Face Spaces: https://huggingface.co/spaces
- Streamlit Cloud: https://streamlit.io/cloud
- GitHub Pages: https://pages.github.com/

---

## YOUR COMPETITIVE ADVANTAGES

1. **Master's degree** in Signal Processing (credibility)
2. **Published research** (thesis + project papers)
3. **Real-world data experience** (FEARLESS STEPS, Apollo-11)
4. **End-to-end skills** (research → production)
5. **German location** (speaks to German companies naturally)
6. **Specific domain expertise** (speech AI, not generic "AI engineer")

---

## FINAL WORDS

This is an aggressive but achievable plan. You're not competing against:
- Course graduates (you have a Master's)
- Theory-only researchers (you deploy code)
- Generic "AI engineers" (you have specialized skills)

You're competing against:
- Other qualified ML engineers (maybe 50 total in German market)
- Most of whom are already employed (internal promotion competition is low)

**The market is hungry for ML engineers.** Germany has 935+ AI startups. They need people like you.

**Execute this plan diligently, and you'll have offers by May 2026.**

---

*Execution starts now. Ship it! 🚀*