Join Emergent in redefining software development. We are creating autonomous coding agents that generate, test, and deploy production applications directly from natural language instructions. Our systems are already powering millions of applications globally and have achieved significant scale and user adoption. We are a venture-backed company comprised of accomplished founders, Olympiad medalists, and industry leaders from top tech companies, focused on solving the complex challenges of AI-driven software creation.

This role is for ambitious builders who thrive on ownership, speed, and making a global impact. We are not just building another developer tool; we are constructing the future of software development itself. As a Staff Engineer on Autonomous Systems, you will be instrumental in architecting the foundational technologies that enable AI agents to reliably produce, validate, and deploy production-grade software at an unprecedented scale.

Architect sophisticated autonomous systems where AI agents generate production-ready software, incorporating robust feedback loops for self-correction and continuous enhancement.

Develop resilient infrastructure capable of supporting millions of applications and real-time workloads, meticulously addressing challenges related to latency, concurrency, and global reliability.

Establish and uphold stringent standards for correctness, observability, and fault tolerance across all backend, infrastructure, and AI components.

Engage in deep collaboration with AI/ML, infrastructure, and product teams to construct comprehensive end-to-end systems, from model interaction layers to advanced distributed execution engines.

Tackle the debugging of non-deterministic systems, engineer solutions for complex edge cases, and build intelligent systems that exhibit reasoning capabilities.

A minimum of 8 years of experience is required in building and scaling large-scale backend or distributed systems.

Demonstrate strong proficiency in Python and/or Go.

Possess a profound understanding of distributed systems, concurrency, parallelism, and system design principles optimized for scale and resilience.

Show a proven history of taking ownership of architectural decisions for critical production systems.

Exhibit the ability to analyze trade-offs, simplify intricate systems without sacrificing functionality, and influence engineering direction across multiple teams.

Experience with AI/ML infrastructure or LLM-based systems is highly advantageous.

Familiarity with technologies such as Kubernetes, Docker, cloud platforms (AWS/GCP/Azure), event-driven architectures, and data infrastructure is considered a bonus.