Research Engineer

Why this role is different

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.

What you'll do;

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 behaviour into concrete research questions, experiments and measurable outcomes.
Investigate agent failures, understand whether problems come from the model, harness, tooling or prompting, and develop ways to improve performance.
Build and improve agent harnesses, internal tools and integrations, including contributing to Aria and other agents used across the business.
Help develop shared knowledge and reusable skills across agents, creating systems that can learn from previous work and improve over time.
Work alongside Infrastructure and Security to ensure agents can operate reliably and safely within our production environment.

What we're looking for;

• Hands-on experience building, evaluating or improving LLM models or agents, whether through professional work, applied research, open-source contributions or substantial personal projects.
• Strong Python skills and good software engineering fundamentals. You're comfortable writing production-quality code and understand the importance of testing, maintainability and reliability.
• Experience with applied research, machine learning or data science, including designing experiments, working with datasets and drawing conclusions from results.
• A good understanding of how LLM agents work beyond prompting, including tool use, harnesses, context management and common failure modes.
• The ability to take an open-ended problem, break it down into testable questions and work independently towards a solution.
• Experience building tools or systems that other people use, with an understanding of how to evaluate their effectiveness beyond technical benchmarks.
• Comfort working across research and engineering, collaborating with different teams and adapting as priorities change.
• Genuine interest in AI agents and how they can be made more capable, reliable and useful.

 

Nice to have;

Experience building LLM evaluation pipelines, benchmarks, reinforcement learning environments or model testing infrastructure.
Contributions to open-source agent frameworks, harnesses, evaluation datasets or benchmarks.
Experience with agent frameworks such as Hermes, OpenClaw or similar tools.
Experience in applied ML research, model training, fine-tuning or reinforcement learning.
An advanced degree or postgraduate research experience in computer science, mathematics, statistics, machine learning or a related field.
Experience in financial markets, quantitative research, DeFi or risk modelling.
Familiarity with AI security considerations, including access controls, prompt injection and data protection.
You don't need prior experience in crypto or DeFi. Strong research and engineering fundamentals matter more, and you'll work closely with people across Gauntlet who understand the financial systems and strategies our agents support.
We expect strong candidates to have experience working in a professional engineering or research environment, but we're more interested in what you've built and how you approach problems than a specific number of years on your CV.

Benefits & Perks;

• Remote first — work from anywhere in the United States

• Regular in-person company retreats and cross-country "office visit" perk

• 100% paid medical, dental, and vision premiums for employees

• $1,000 WFH stipend

• Monthly reimbursement for home internet, phone, and cellular data

• Unlimited vacation

• 100% paid parental leave of 12 weeks

• Fertility benefits

• Opportunity for incentive compensation