Research Engineer

Research Engineer

New York City / San Francisco / Los Angeles / Remote

Engineering / Full Time / Remote

The role Gauntlet is looking for an AI Research Engineer to help improve how we build, evaluate and use AI agents across the business. We already have agents running in production, including Aria, our in-house generalist agent. They are used across research, engineering and business workflows, and we’ve learned a lot from seeing how they perform in practice. Some tasks work extremely well;

others produce inconsistent results, and changes to models or tooling can introduce unexpected problems. Today, much of that feedback is still subjective. We want to build a more systematic way of understanding what works, what doesn’t and why. A major part of this role will be building the evaluation frameworks that allow us to measure agent performance on real tasks.

You’ll work with our engineers, quantitative researchers and other teams to understand where agents struggle, turn those observations into measurable experiments, and use the results to improve the systems we’re building. You’ll also contribute directly to our existing agents and internal tooling. Initially, the work will be more engineering-focused as you get familiar with our systems, shipping improvements and integrations that teams can use.

Over time, you’ll take on more open-ended research, helping us develop better approaches to evaluation, agent behavior and infrastructure. We’re also thinking beyond individual agents. One of our longer-term goals is to build a shared system of knowledge and reusable skills that our agents can draw from and improve over time. You’ll play an important role in developing that infrastructure and how we evaluate its effectiveness.

This is an applied research role with a meaningful engineering component. We’re looking for someone who enjoys investigating difficult problems, can design good experiments and is comfortable building and shipping the solutions that come out of them.

Gauntlet builds the financial systems of the future. While much of onchain finance is focused on point solutions, we operate across the entire stack to offer best-in-class vault products. Today we serve over $1.7B in client TVL across some of the largest fintechs/neobanks, protocols, exchanges, and capital allocators in crypto - and, increasingly, traditional asset management. Our team brings together traditional finance and crypto-native expertise to deliver durable, sophisticated products for institutional clients moving onchain.

This is an opportunity to work at the intersection of AI, quantitative research and DeFi, tackling problems where advances in AI can have a direct impact on financial markets and onchain strategies. You’ll go beyond building individual agents, helping define how they’re evaluated, how they learn from real-world tasks and how their capabilities scale across the business. With few established benchmarks in this space, you’ll have the freedom to explore new approaches and take your ideas from research through to production, working directly with the teams putting them to use.

• Build evaluation frameworks for AI agents, including datasets, sandbox environments and metrics that measure performance on real-world tasks.
• Design experiments to compare models, agent harnesses, tools and execution environments, identifying regressions and understanding the trade-offs between performance, reliability and cost.
• Work with teams across Gauntlet to turn qualitative feedback about agent behavior into concrete research questions, experiments and measurable outcomes.
• Investigate agent failures, understand whether problems come from the model, harness, tooling or…