Personal data
基本情報- Name 氏名
- Peter József Lódri · ロドリ・ペーテル・ヨージェフ
- Born 生年月日
- 16 July 1992 · Tatabánya, Hungary ハンガリー出身
- Nationality 国籍
- Hungary (Hungarian) ハンガリー
- Address 現住所
- Budapest, Hungary — relocating to Tokyo 東京へ移住予定
- Contact 連絡先
- peter.lodri@gmail.com (primary) · cabotage@pm.me · +36 20 391 5161
- Availability
- Available immediately · full CET/JST overlap · EU work authorization (no sponsorship required)
Education
学歴(高校以降すべて)| 期間 / Period | 学校・専攻 / Institution & field |
|---|---|
| 2011 – 2016 | University / College — degree programme
【要記入】 school name, city, degree, field of study |
| 2007 – 2011 | Secondary school 高等学校 — high school
【要記入】 school name, city, course / specialisation |
Fields marked 【要記入】 are facts only you can confirm — the résumé is otherwise complete.
Professional experience
職務経歴書| 期間 / Period | 職務 / Role |
|---|---|
| 2024.05 – present | Senior Software Engineer — Plandek
Engineering metrics & analytics platform. Build and maintain analytics features across the platform; review code across a polyglot stack.
TypeScript · React · Python |
| 2023.05 – 2024 | Senior Software Engineer — Virality Data
Large-scale social-media scraping (millions of videos). Designed and operated high-throughput ingestion and processing pipelines.
Kafka · Puppeteer · Celery · Pandas · AWS · JavaScript · Python |
| 2022 – 2023 | Senior Software Engineer — ASAPP Inc.
AI-powered customer-service platform for Fortune-100 companies. Improved customer–agent response time by 50–70% via a microservices architecture.
Go · Kotlin · Scala · Kubernetes · Kafka · AWS |
| 2022 | Lead Go Developer — DailyWire.com
Streaming platform with 110k concurrent users. Led the backend team for an integrated chat + video streaming platform.
Go · Python · AWS |
| 2021 – 2022 | Senior Python Developer — various fintech (Sixth Street Partners · Citadel · Morgan Stanley)
Modernized trading software stacks from legacy systems to cloud-native ETL.
Python · Bash · AWS · Docker |
| 2019 – 2021 | Lead Python Backend Developer — Cygnet Dooel
Advanced cyber-intelligence solutions. Designed mission-critical security infrastructure and led offensive-security product development; mentored new hires in advanced Python.
Python · AWS · Android · C · Go · Java · Kubernetes |
| 2017 – 2019 | Python Developer — Inpedio
Mobile security solutions. Built custom Android security/antivirus tooling and messaging behaviour-analysis tools; implemented a successful
kptr_restrict bypass.Python · C · Android · Java · Go |
| 2016 – 2017 | Software Developer — Saltech Consulting
Built a custom UI testing framework for Siemens.
Python · GCP · Java · PEGA |
Skills & languages
スキル・語学WebGL2, Web Audio, vector/oscilloscope synthesis, shader feedback, DSP.
C, Rust, and Swift close to the metal — SIMD, memory layout, quantization, zero-dependency binaries.
Go, Python, TypeScript; microservices, Kafka, Celery, high-concurrency streaming, ETL.
Cloudflare Workers/Pages, D1/KV, AWS, GCP, Kubernetes, Docker, custom domains, deploy pipelines.
Reverse engineering, Android internals, eBPF, pentesting.
Ternary/BitNet quantization, MLX on Apple silicon, model loading, evaluation, honest reporting.
English · Hungarian (native) · Spanish · Latin · Esperanto · Romani · Macedonian; basic Mandarin & Japanese (studying).
C · Rust · Python · Go · TypeScript/JavaScript · Java · Kotlin · Scala · Ada, and esoteric languages generally.
Portfolio & code samples
制作実績・作品URLWork samples / 制作実績
Interactive CV, drawn live in the browser (an example of the work itself): teamlab.vaked.dev · vaked.dev
Source & code samples / ソースコード: github.com/peterlodri-sec
Team vs. individual, and scope / チームか個人か・担当範囲
All work above was built individually, end-to-end — concept, design, math/algorithm decisions, implementation, and verification. In professional teams (ASAPP, DailyWire) I have led backend teams and owned subsystems, so I am equally comfortable as an individual contributor and as the bridge between artists and engineers.
AI usage disclosure
AIの利用に関する注意事項提出物の開発において、AIコーディングアシスタント(Crush 経由のLLM)を補助的に使用しました。使用範囲:コードの雛形生成、ドキュメント整備、テスト/検証スクリプトの作成、リファクタリング補助。人間が担当:企画・設計・数学/アルゴリズムの決定、ビジュアルとサウンドの方向性、リアルタイム描画や量子化の実装判断、最終確認と検証(ハッシュによる再現性チェック)。
I used an AI coding assistant (an LLM via Crush) as a support tool: scaffolding, docs, test/verification scripts, refactors. I — the human — did the concept, design, the math/algorithm decisions, the visual and audio direction, the real-time and quantization implementation calls, and the final verification (reproducibility by hash). I can walk through the exact scope per work in the interview.