Jobs at Auger.AI (Now Hiring) — 6 open

About the Team & Role

• We'd rather see your checkpoint get quantized, distilled, and forked into someone else's production stack than see it top a leaderboard for a week and disappear. A few things from The Auger Edge show up again and again in the people who do well here.
• The instinct to Explore to Evolve looks like this in practice: you don't just call .fit() on a technique, you can derive why it works, and you'll rebuild the pipeline from the tokenizer up when the domain demands it, whether that's continued pretraining into a knowledge-intensive vertical or an eval harness that measures something real instead of something convenient.
• If you've built evaluation frameworks specifically to catch what standard benchmarks miss, you're already living Own the Fall, Rise Stronger: you treat a bad eval run as signal, not shame, and the loop from "here's where it breaks" to "here's the next checkpoint" is short.
• The field dresses complexity up as sophistication constantly, which is exactly what it means to Crush Complexity here: we want the person who ships the clean dataset and the clean eval that a teammate can pick up cold, not the clever bespoke pipeline only its author can operate.
• Tech-leading through v1, v2, v3, each release measurably stronger than the last, is what All In, All the Time looks like day to day, and it's also why your job isn't done at a passing eval or a merged PR. It's done when you've watched the checkpoint run flawlessly in production, under real load, on real customer data. Ask anyone who's been here a while what that means in practice: the job is never actually done, there's always a v4.

• You've built foundational training data at scale, corpora and not just models, and understand that what goes into a model matters as much as its architecture.
• You've led a project across multiple release cycles, each one measurably better than the last.
• You've designed evaluation methodology that goes beyond standard benchmarks, built specifically to surface what those benchmarks miss.
• You've adapted general purpose models to specialized, knowledge intensive domains and understand what actually transfers versus what has to be rebuilt.
• You've created datasets that other researchers and practitioners now build on.
• You've taken research past the paper and into a real, end to end system that people other than researchers actually use.
• Recognition, best paper or outstanding paper or otherwise, has followed the work, but wasn't the point of the work.