875 karma · joined December 14, 2020
How so?
I would substitute "deeply" for "superficially". Like, if my parents found some way to prohibit porn when I was an adolescent, I wouldn't say they cared deeply about me. I would say they were misguided and authoritary. The "care deeply" idea you are putting forward is just trying to distil whatever societal norm currently is into the youngs.
Remote: Yes
Willing to relocate: Yes
Technologies: Python, Deep Learning (PyTorch), NLP, Graph Knowledge Systems, Vector Search (Cosine Similarity), PostgreSQL, React/Svelte.
Résumé/CV: https://www.linkedin.com/in/rodrigo-heck-7280a218a/
Email: rodrigo.heck29@gmail.com
Hi HN, I’m Rodrigo. I’m a software engineer working at the intersection of applied ML and scalable systems. Lately, I’ve been obsessed with solving the "context window" problem through graph-based knowledge representations. My recent work focuses on treating graphs not just as data stores, but as a method for information compression and long-term memory permanence in AI agents. I develop pipelines that integrate LLMs with structured memory to allow for more efficient knowledge generalization and recall.
Key areas of expertise:
1. AI Memory Architectures: Building persistent, graph-oriented systems for reasoning and long-term retrieval.
2. ML Systems Engineering: Developing TTS systems, semantic search tools, and RAG pipelines that go beyond simple vector lookups.
3. Full-Stack Foundations: Bridging the gap between a PyTorch model and a production-ready Svelte/React interface, backed by robust Linux/Postgres infrastructure.
I’m looking for a role where I can contribute to the "Next Step" of LLM integration—moving past simple chat interfaces toward systems with true persistent memory and structured reasoning.
Remote: Yes
Willing to relocate: Sure
Technologies: Deep Learning (PyTorch), ReactJS, Svelte, Natural Language Processing (NLP), Python (Flask, OpenAI API), Graph Knowledge Systems, Vector Search (Cosine Similarity), PostgreSQL, Linux System Administration
Résumé/CV: https://www.toptal.com/resume/rodrigo-heck
Email: rodrigo.heck@toptal.com
Lately, I’ve been exploring graph-based knowledge representations as a method for information compression and long-term memory permanence in AI systems — developing pipelines that integrate LLMs with structured memory to retain and generalize knowledge efficiently.
My background bridges applied machine learning and software engineering, from building text-to-speech systems and semantic retrieval tools to experimenting with persistent, graph-oriented architectures for reasoning and recall. I’m looking for opportunities at the intersection of AI systems engineering, knowledge representation, and scalable memory architectures.