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| # 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! 🚀* | |