- Clear ownership, not decision by consensus
- First principles over inherited patterns
- Shipping systems, not slide decks
- Fast feedback from reality, not opinions
- 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.
Skills Required
- Built foundational training data and large domain-specific corpora at scale
- Led a research project across multiple release cycles with measurable improvements
- Designed evaluation methodologies that surface real-world failures beyond standard benchmarks
- Adapted general-purpose models to specialized, knowledge-intensive domains
- Created datasets used by other researchers or practitioners
- Taken research into end-to-end production systems used by non-research teams
- Experience with model compression/production techniques (quantization, distillation) and tokenizer/pipeline engineering
What We Do
About Auger Auger is a pioneering venture to build the world's first true end-to-end supply chain operating system. Founded and led by Dave Clark, former CEO of the Amazon Consumer Business and backed by an initial $100M from Oak HC/FT, Auger is building a future where global supply chains operate with the simplicity of today’s most intuitive consumer technologies. Revolutionizing global supply chains with an AI-powered OS unifying data for seamless, real-time insights, and powerful automation. Our Solution Auger is creating a new solution for companies seeking better options. Auger’s core strength lies in its deep AI-powered automation, paired with a consumer-grade user experience. This combination allows operators to handle complex tasks through simple, familiar tools. Need real-time inventory insights for next week’s shipment? Just ask. Actionable data appears instantly, enabling swift decisions—no complex queries or training required. Why We’re Different Traditional supply chain management is fragmented, relying on incompatible systems and inefficient workarounds. Many companies are stuck with “Franken-software”—patched-together solutions that fail to communicate effectively. Auger is different. We integrate deeply with existing systems, use AI to automate routine processes, and deliver a cohesive user experience that feels intuitive and natural, letting your team focus on what matters: driving growth, innovation, and sustainability. A Human-Centered Approach Broken supply chains don’t just impact businesses—they affect people. Delays mean products don’t reach shelves, miscommunications lead to overtime and burnout, and inefficiencies drive up costs and contribute to a growing carbon footprint. We believe supply chain problems are human problems, and we’re here to solve them. At Auger, we’re on a mission to make global supply chains more efficient, more sustainable, and ultimately, better for everyone.
