The Role
Lead research on AI-assisted software development, focusing on developing LLMs for coding tasks and improving human-agent interactions.
Summary Generated by Built In
About us
Imbue's mission is to make "open" AI agents win over closed agents. To do that, we’re starting with making open coding agents, since they both make it easier to construct future agents, and we can use them ourselves.
Our flagship product, Sculptor, gives engineers the power to run and coordinate multiple coding agents in parallel, helping them move faster and stay in flow. At its core, Sculptor is about giving developers power to make software creation easier, faster, and more reliable. We intend to make the source code available to both serve as an example of how to create open agents, and provide a tool that makes it easier for others to create open software. The insights we gain from building Sculptor help us both understand the interaction design and infrastructure necessary for creating open agents, and improve the capabilities of the underlying models.
Ultimately, we aim to rekindle the dream of the *personal* computer, where computers become truly intelligent tools that empower us, giving us freedom, dignity, and agency to pursue the things we love.
Summary
In this role, you’ll build the AI systems that power Sculptor and ensure that coding agents work “for you”. You’ll directly shape the intelligence behind the product you use every day, and will have real influence on the problems we tackle and the approaches we take. We're looking for scientists who combine research excellence with pragmatic engineering; someone who can push the boundaries of what's possible while shipping real solutions that delight our users.
What you'll do
• Build verification systems that make coding agents more reliable, including test generation and LLM-as-judge approaches
• Turn ambiguous problems into quantifiable ones, through a mixture of annotation, synthetic data generation, and product logging
• Create agent improvement loops via self critique and evolutionary algorithms
• Build experiences and tooling that help developers easily audit and manage coding agents
• Partner with the product team to translate cutting-edge applied research into features users actually want
You are
• Driven by making AI agents that actually work in the real world
• Excited to see your research ship to thousands of developers
• Independent and self-motivated
• Comfortable with Python, LLMs, and agentic approaches
Compensation and Benefits
• Support for self-improvement: coaching, courses, conferences, etc
• Company offsites—past locations include NYC, Santa Cruz, Hawai’i, and Tokyo!
• Company paid medical, dental, and vision for you and your dependents
• Lunch provided daily for onsite employees
• $250 lifestyle stipend per month
• Flexible PTO
• Frequent team events, dinners, and fun activities
• Compensation packages are highly variable based on a variety of factors. If your salary requirements fall outside of the stated range, we still encourage you to apply. The salary range for this role is $170,000–$400,000.
How to apply
All submissions are reviewed by a person, so we encourage you to include notes on why you're interested in working with us. If you have any other work that you can showcase (open source code, side projects, etc.), certainly include it! We know that talent comes from many backgrounds, and we aim to build a team with diverse skillsets that spike strongly in different areas.
We try to reply either way within a week or two at most (usually much sooner).
Learn more about our full interview process here.
Top Skills
AI
Coding
Llms
Machine Learning
Reinforcement Learning
Software Development
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
We build AI systems that can reason, in order to enable AI agents that can accomplish larger goals and safely work for us in the real world. To do this, we train foundation models optimized for reasoning. On top of our models, we prototype agents to accelerate our own work, seriously using them in order to shed light on how to improve the underlying model capabilities, as well as the interaction design for agents.
We aim to rekindle the dream of the *personal* computer—for computers to be truly intelligent tools that empower us, giving us freedom, dignity, and agency to do the things we love.
Gallery
