Why I Started The Artificial Engineer

The Problem

After my 23rd attempt at debugging an inference pipeline at 2 AM on a Sunday I realized most ML tutorials end exactly where real problems begin.

My Path Here

I didn’t come from a traditional CS background—I studied economics and law. But curiosity (and a lot of debugging in PyTorch) led me into machine learning.

Over the last 4+ years, I’ve worked on:

• Evaluation frameworks such as Dynabench
• Large-scale datasets such as People’s Speech and Adversarial Nibbler
• Publishing at top conferences
  • Best paper award at NeurIPS D&B 2024 with The Prism Alignment Dataset
  • BabyLM at CONNL (ACL) 2023
  • DataPerf at NeurIPS 2023
• Some competitions every now and then such as PatentBot

Through this work, I noticed a consistent pattern: the gap between research papers and production reality is bigger than anyone admits.

• Technical content: often too dense for anyone outside the research bubble
• Popular ML content: often oversimplified to the point of being misleading
• Bridges between the two: almost nonexistent

That’s the problem this blog tries to solve.

Two Tracks, One Mission

This blog runs on two parallel tracks—each aimed at closing the gap between research depth and real-world clarity.

🔧 For Engineers

Deep technical dives, reproducible experiments, and hard-won lessons from production. Sample posts:

• Why our LangChain pipeline breaks when trying to customize it
• Scaling inference when GPUs are scarce (and expensive)
• Fine-tuning small LLMs for common use cases — a cheaper path than you think
• Building a production-ready ASR pipeline: from VAD to deployment

🌍 For Everyone

Clear, accessible explainers of how ML shapes daily life — no PhD required, no jargon walls. Sample posts:

• Why LLMs hallucinate (and why it matters in practice)
• Getting more out of your AI assistant without burning tokens
• Tips for better image generations with today’s generative models
• How recommendation systems really guess your taste

Why “The Artificial Engineer”?

I’m not a “real” engineer by degree, but I’ve spent four years making AI systems work in the real world. The name captures the irony: someone artificial (by training) engineering artificial intelligence.

Join the Conversation

I’ll be posting twice a month (one per track). But I want this to be a dialogue, not a broadcast.

Every ML engineer has that moment when the tutorial ends and reality begins. I’m documenting everything that happens next—the debugging, the scaling, the “why didn’t anyone mention this?” moments. Subscribe below to follow along.