Senior Backend Engineer | Seattle | $250k + meaningful equity

Harrison Clarke; Seattle (WA); On-site; USD 160,000 - 210,000; Full time

Job summary

Harrison Clarke is partnering with a fast-growing AI company to build the backend for an enterprise AI economics platform. You will develop scalable services, APIs, and data pipelines to process AI usage, cost, and performance data.

This is a highly technical role with ownership from design to production. Ideal candidates have 8+ years in software engineering, strong backend language skills (Python/Go/Java), and a deep understanding of distributed systems, databases, and APIs.

Qualifications

• 8+ years of professional software engineering experience.
• Experience with building backend services and distributed systems.
• Experience in cloud infrastructure and deployment practices.
• Strong communication and product-focused engineering approach.

Responsibilities

• Design and build scalable backend services powering the core platform.
• Build APIs and services that expose AI usage, cost, performance data.
• Develop systems handling high-volume, real-time telemetry from GenAI apps.
• Work with data pipelines and distributed systems to transform AI activity into insights.
• Own projects end-to-end from design through deployment and iteration.
• Establish engineering best practices as the team scales.

Skills

Distributed systems

APIs

System design

Tools

Python

Go

Java

Job description

Harrison Clarke are partnered with a fast-growing, early-stage AI company building the financial and operational infrastructure powering the next generation of enterprise AI.

As enterprises move beyond experimenting with GenAI and begin deploying AI agents and applications at scale, they’re running into a fundamental challenge: how do you understand what AI is actually costing, the value it’s generating, and which initiatives are worth investing in and scaling?

This company is building the platform designed to answer exactly that.

The Role

As a Backend Engineer, you’ll play a key role in building the infrastructure behind an enterprise-grade AI economics platform.

You’ll work across backend systems that ingest, process, analyze, and expose high-volume data generated by AI applications and agentic workflows. You’ll solve challenging problems across distributed systems, data processing, APIs, observability, reliability, and scalability, while working closely with the founding team.

This is an opportunity to join a small, highly technical team where you’ll have meaningful ownership and a direct impact on both the product and the underlying architecture.

Responsibilities:

• Design and build scalable backend services powering the core platform
• Build APIs and services that process and expose AI usage, cost, performance, and business-impact data
• Develop systems capable of handling high-volume, real-time telemetry from GenAI applications
• Work with data pipelines and distributed systems to transform raw AI activity into actionable insights
• Build integrations across AI models, cloud providers, agent frameworks, and enterprise technology platforms
• Contribute to the architecture and technical direction of the backend platform
• Design for reliability, scalability, performance, and observability from the outset
• Work closely with product and engineering to turn ambiguous problems into simple, scalable solutions
• Own projects end-to-end, from technical design through implementation, deployment, and iteration
• Help establish engineering best practices as the team and product scale

Qualifications:

• 8+ years of professional software engineering experience
• Strong proficiency in Python, Go, Java, or another modern backend language
• Strong understanding of distributed systems, APIs, databases, and system design
• Experience building systems that process high volumes of data or events
• Experience with cloud infrastructure and modern deployment practices
• Strong understanding of reliability, observability, and performance considerations
• Comfortable operating in an early-stage environment with significant autonomy
• Ability to take ownership of ambiguous technical problems and drive them through to production
• Strong communication skills and a pragmatic, product-oriented approach to engineering