Research Scientist

About the Role

Auger is the autonomous operating system for supply chains — the layer that finally allows disparate systems like ERP, WMS, and TMS to work together instead of against each other. Our AI detects disruptions, evaluates trade-offs, and executes decisions automatically — moving from signal to action in seconds, not weeks. We eliminate the Coordination Tax: the billions in capital and time lost when disconnected systems force the best people in the business to become the Human API between planning and execution.

At Auger, we design autonomy into our systems. We expect the same from our people. That means:

• Clear ownership, not decision by consensus
• First principles over inherited patterns
• Shipping systems, not slide decks
• Fast feedback from reality, not opinions

Auger was founded by Dave Clark and is backed by $150M from Oak HC/FT and Eclipse Capital. Headquarters in Dallas, TX and Bellevue, Washington.

About The Team & Role

The core of this work is training foundational models for supply chain expertise, not wrapping a generalist frontier model in a better prompt. We think a specialist model, trained deep on the domain, beats a generalist model on the problems that actually matter here, and gets us to a level of inference speed, cost, and reliability that routing every decision through a frontier API simply can't reach.

What Makes You Succeed Here

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:

• Explore to Evolve: 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.
• 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.
• Crush Complexity: 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.
• All In, All the Time: Tech-leading through v1, v2, v3, each release measurably stronger than the last. 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.

Requirements

• 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, outstanding paper, or otherwise) has followed the work, but wasn't the point of the work.

We're not hiring for a specific problem or a specific product. We're hiring for a pattern. If you read that list and thought "yes, and also," we want to talk to you.