Senior Infrastructure Engineer

Description:

We're looking for a Senior Backend Engineer to own core infrastructure that powers our platform - from the pipelines that move and process data at scale, to the systems that keep our engineering org shipping fast and reliably. This is a technical leadership role: you'll set architecture direction, raise the bar on engineering practices, and mentor other engineers, while staying deeply hands-on.

You'll operate in an organization that expects AI-native ways of working. Using tools like Claude, Cursor, Hermes or LangGraphor isn't a bonus here - it's the baseline for how we build, and we expect you to help the rest of the team level up on it too.

• Own core backend infrastructure - design and build scalable, reliable Node.js/TypeScript/Python/Go services that other teams and systems depend on.
• Build and scale data pipelines - architect the infrastructure that processes and moves large volumes of data reliably, efficiently, and at scale.
• Elevate test & validation infrastructure - build the automation, tooling, and CI/CD systems that let engineering ship fast without breaking things, and that make quality and reliability measurable.
• Set technical direction - make architecture decisions with company-wide impact, drive design reviews, and establish engineering standards and best practices.
• Mentor and multiply - level up other engineers technically, and help shape a fast, high-ownership engineering culture as the team grows.
• Lead with AI-native workflows - use AI-assisted development tools daily to move faster without sacrificing architectural quality, and champion their adoption across the team.

• 8+ years of backend software engineering experience, including end-to-end ownership of production systems at scale.
• Deep expertise in Node.js and TypeScript, with strong software architecture skills for building modular, distributed systems.
• Strong cloud and infrastructure experience (AWS, Kubernetes, Docker), including CI/CD, observability, reliability, and production operations.
• Experience building and scaling data-intensive systems, including high-throughput data pipelines, distributed APIs, data modeling (SQL & NoSQL), and messaging platforms such as Kafka or Spark.
• Experience building developer infrastructure, including internal tooling, test automation, validation frameworks, or agent-facing infrastructure (e.g., MCP servers, AI/LLM tools and APIs).
• AI-native engineering mindset, with daily expert use of AI-assisted development tools (Claude Code, Cursor, Copilot, or similar) to significantly improve engineering velocity while maintaining quality.
• Technical leadership experience, including mentoring engineers, driving architecture decisions, and raising engineering standards across a team.
• Experience working in fast-paced startup or scale-up environments, delivering for customers under short SLAs.