About the role
Emergent builds autonomous coding agents that replace traditional software development by generating, testing, and deploying production applications directly from plain-language intent. Our systems run in production at global scale and are used to build millions of real applications.
Since our public launch, we've crossed $100M in ARR and grown to over 10M users across 190+ countries. We're backed by Khosla Ventures, SoftBank, Google, Lightspeed, Prosus, Together, and Y Combinator.
We're solving the hard part of AI-driven software creation: correctness, reliability, security, and scale in real production systems. The team is built by repeat founders, Olympiad medalists, IIT & IIM alumni, and leaders from Google, Amazon, and Dropbox.
We're hiring builders who want ownership, speed, and impact at global scale.
The Role: We're building AI agents that plan, build, test, and ship real software at global scale. Your job is to turn raw LLM and system capability into measurable, shippable gains in how well our agents actually perform. You own the loop end-to-end: what we try, how we prove it's better, what goes live, and what gets rolled back. This role sits at the intersection of product, engineering, and applied research. You won't just ship features. You'll decide what makes the agent better, how we measure it, and what's ready to go live at the scale of millions of real applications.
What You'll Do:
- Develop deep, evidence-grounded intuition for how the agent thinks, succeeds, and fails across the full range of real-world use
- Mine production behavior for the failure modes, regressions, and bottlenecks most teams never see, and turn them into clear, quantitative signal
- Define and run high-leverage experiments that improve agent quality, reliability, and code outcomes, spanning prompt tuning, eval dataset creation, experimentation, and harness engineering
