Etherialize - Applied AI Engineer

We all know that software engineering is getting turned on its head.

It’s easy to pop open Claude Code and ask for help with small or repetitive tasks. But as the potential of these tools expand, questions emerge:

• How do we build the infrastructure and workflows to make the most of new advancements?
• How do we share context across the team to make these tools even more capable?
• And, most importantly, how do we keep code quality to the standard needed to support institutional finance, while staying close to the cutting edge?

This is where you come in.

We’re looking for an exceptional Applied AI Engineer to join our engineering team. You’ll be responsible for solving the problem:
What does it look like for a team building mission critical software to use AI to its maximum potential?

You’ll work side‑by‑side with our engineers in Austin, building the tools and processes to keep us at the cutting edge, and using those tools to contribute to real production code.

Your mission is to level up the entire organization’s impact and output by embedding AI deeply into how we engineer, operate, and make decisions.

You will:

• Accelerate engineering velocity with AI:
  The frontier of AI development is moving fast, but the frontier of applying these advances to mission critical systems lags far behind. Your job is to solve for what this looks like today, and continually reassess the right balance as the tools improve.
• Contribute meaningful production code:
  This is a real engineering role. You won’t know the bar we need to hit if you aren’t capable of delivering it yourself. You’ll contribute directly to our core product and use AI to amplify your own impact, ensuring the workflows you build actually work in production.
• Help shape AI functionality in our flagship product:
  Partner closely with product and engineering to ideate, prototype, and structure AI-powered product features.
• Systematize AI usage across the team:
  Help identify and implement high-leverage AI use cases across the company and evolve our team from “everyone uses AI” to “everyone is effectively managing a team of AIs,” with consistent quality, guardrails, and workflows.
• Stay ahead of what’s coming:
  Constantly experiment with new models, tools, and workflows, and exercise strong judgment about what’s worth adopting for our company. Be the go‑to person others learn from when it comes to AI, and share learnings so adoption actually sticks across the team.

• Career-defining scope. You’ll help define how teams building mission critical systems work with AI. We’ll likely release our systems, thinking, and open source code publicly, and you’ll be at the forefront of these breakthroughs.
• Nerd out all day. While many engineers have to save their AI rabbit-holing for nights and weekends, it’ll be the core of your day to day. Your job is literally to experiment, learn and develop the coolest things you can think of.
• Work with an exceptional team. Work closely with Zach Obront (CTO), Danny Ryan (President), and engineers from Goldman Sachs, Circle, Bloomberg, Kraken, NEBRA, and more.

You are a strong engineer first, and deeply obsessed with what AI makes possible. These past few months have likely been some of the most exciting of your career, filled with late nights experimenting with new ideas.

You are likely:

• A senior or mid-senior engineer who has already shipped and owned meaningful systems in production, with strong system-design instincts.
• Obsessed with how AI is evolving software engineering. You code every day, and your family may be starting to worry whether you like Claude more than them.
• Solutions-driven and personable — you take pride in being the go‑to person who unblocks others, not just in what you personally build.
• Gleefully curious about optimization, efficiency, and what engineering teams will look like years into the future.
• Comfortable operating in ambiguity and grounding big ideas in production reality.
• Likely contributing open-source work, side projects, or public experimentation with AI productivity tools because you just can’t help yourself.
• You treat your AI infrastructure as a production system to be engineered.…