AI Research Engineer
New
G
GauntletAI agents, DeFi
Remote first — work from anywhere in the United States. Hiring is reserved for potential employees who are able to work within the contiguous United States and Canada.Full-Time
Salary$175,000 - $195,000 base plus additional On Target Earnings potential by level and equity in the company.
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Job Details
- Experience
- 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.
- Required Skills
- PythonMachine LearningContent managementData scienceSoftware Engineering
Requirements
- Have hands-on experience building, evaluating, or improving LLM models or agents through professional work, applied research, open-source contributions, or substantial personal projects.
- Demonstrate strong Python skills and sound software engineering fundamentals, including production-quality code, testing, maintainability, and reliability.
- Have experience with applied research, machine learning, or data science, including designing experiments, working with datasets, and drawing conclusions from results.
- Understand LLM agents beyond prompting, including tool use, harnesses, context management, and common failure modes.
- Be able to break open-ended problems into testable questions and work independently toward solutions.
- Have experience building tools or systems for others and evaluating their effectiveness beyond technical benchmarks.
- Be comfortable working across research and engineering, collaborating with different teams, and adapting as priorities change.
- Have a genuine interest in AI agents and improving their capability, reliability, and usefulness.
Responsibilities
- Build evaluation frameworks for AI agents, including datasets, sandbox environments, and metrics for real-world tasks.
- Design experiments comparing models, agent harnesses, tools, and execution environments, and identify regressions and performance, reliability, and cost trade-offs.
- Turn qualitative feedback about agent behavior into research questions, experiments, and measurable outcomes with teams across Gauntlet.
- Investigate agent failures and determine whether issues stem from the model, harness, tooling, or prompting.
- Build and improve agent harnesses, internal tools, and integrations, including contributing to Aria and other business agents.
- Develop shared knowledge and reusable skills across agents so systems can learn from previous work and improve over time.
- Work with Infrastructure and Security to support reliable and safe operation of agents in production.
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