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Conduct is building an AI operating system designed to absorb IT complexity for the world's largest enterprises. The company recently closed a $60M Series A and is scaling rapidly with major enterprise customers already in production.
As a Backend Engineer, you will have broad ownership across architecture, developer experience, and production systems. You'll work on genuinely hard infrastructure problems: processing tens of millions of lines of code per customer, generating large-scale LLM workloads under enterprise-grade reliability requirements.
Key responsibilities include:
- Accelerating product teams by building platform abstractions, sensible defaults, and internal tooling that compounds value
- Scaling with confidence by designing and maintaining infrastructure that meets stringent enterprise reliability requirements without becoming fragile as the company grows
- Taming LLM infrastructure, handling high token throughput, real-time usage tracking, and flaky provider management to ensure reliability
- Raising the floor across systems like auth, observability, and job orchestration—building well-designed systems from the start that don't need rebuilding later
- Working end-to-end across APIs, infrastructure, and data flows with strong system design instincts
The tech stack includes Kotlin backend (paired with the Java ecosystem), React/Vite/TanStack frontend, Temporal and PortKey for durable execution and LLM routing, and a specialized LLM harness built from production experience.
The team values extreme ownership, high velocity, low-ego collaboration, and pragmatic problem-solving. You'll be part of a small, talent-dense team doing foundational work that defines the company.
REQUIREMENTS:
- 3+ years of backend engineering experience with exposure to platform or infrastructure work
- Demonstrated ability to scale systems from MVP to production-grade
- Strong system design instincts and end-to-end ownership across APIs, infrastructure, and data flows
- Experience with distributed systems, scaling databases (PostgreSQL), queueing, and large-scale batch processing
- Ability to benchmark and tune systems under real load and make pragmatic trade-offs with ambiguous data
- Familiarity with LLM infrastructure at scale
- Track record of raising the bar through code, mentorship, and better systems
- Pragmatic, high-agency mindset with comfort in ambiguity
