# 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! 🚀*