Software Engineer, Sift

• 6+ years of professional software engineering experience, with significant experience in developer infrastructure, test infrastructure, platform engineering, or developer tooling
• Deep hands-on experience with Docker, Kubernetes, build systems, and deploying applications to cloud platforms such as AWS, GCP, or Azure
• Experience building and automating developer infrastructure, deployment systems, or testing environments, with an understanding of scalability, performance, and reliability
• Experience developing or optimizing CI/CD pipelines and automating infrastructure provisioning and deployments
• Strong software engineering and programming skills, preferably in Go or Python
• Ability to balance infrastructure performance, environment fidelity, reliability, and cost
• Strong collaboration skills and a developer-focused mindset, with experience building tools that improve engineering productivity

Desirable

• Sift is hiring a Senior Software Engineer to build and own the developer infrastructure and testing environments that enable our engineers to ship software faster and with greater confidence
• You'll design systems that allow engineers to spin up complete, production-like environments on demand to test changes, simulate real-world workloads, and validate correctness. As AI coding agents become increasingly integrated into development workflows, you'll build the infrastructure that enables these agents to independently test, debug, and validate the code they produce
• Sift processes high-volume telemetry from complex hardware systems, making realistic testing environments particularly challenging. This is a foundational, high-ownership opportunity to architect and scale the infrastructure that powers how we develop, test, and ship software
• Design and build infrastructure that provisions isolated, production-like testing environments on demand, enabling engineers to validate changes against Sift's full application stack
• Build and optimize CI/CD pipelines, build systems, and deployment automation to make testing environments fast, reliable, and cost-efficient
• Develop infrastructure that supports diverse testing workloads, including integration testing, correctness validation, load testing, and performance testing
• Build tooling for realistic data generation and user behavior simulation to validate software under production-like conditions
• Enable AI coding agents to interact with testing environments, execute tests, and validate code changes autonomously
• Partner closely with engineering teams to understand developer needs, eliminate bottlenecks, and improve development velocity and software quality