Software Engineer, AI Productivity

As a Software Engineer focused on AI productivity, you will build and roll out the tools that help us use AI effectively across the company. You will work closely with engineering, research, operations, people, and other teams to understand how people work, identify where AI can create leverage, and turn those opportunities into reliable internal tools and workflows.

The Team

Runtime owns core systems that help π’s operations and research teams move quickly and reliably. This role will sit in Runtime and work alongside engineers focused on build systems, infrastructure, and developer productivity.

Your focus will be AI tooling: making AI agents, assistants, integrations, and automation useful across the company. You will partner deeply with teams across PI to understand their workflows, build tools that fit how they work, and drive adoption until those tools become part of the operating rhythm of the company.

In This Role You Will

- Own AI tooling adoption across π: Identify where AI tools can improve velocity, build or integrate the right solutions, teach teams how to use them, and drive adoption.

- Build internal AI tooling and integrations: Build backend services, scripts, workflows, user interfaces, LLM integrations, and agent infrastructure.

- Make AI agents ergonomic: Own workflows for cloud agents, agent management, and internal automation that are easy to use, easy to monitor, and easy to trust.

- Build tools for engineering, research, and operational velocity: Help engineers use AI to write, test, debug, review, and validate code faster. Empower researchers to extract signals and iterate quickly and confidently. Work with operations and recruiting to understand their workflows and build tools that give them leverage.

- Own best practices and enablement: Create playbooks, examples, onboarding, office hours, demos, and shared workflows that help people learn from the best AI users at π.

- Partner on security and data access: Ensure AI tools have the right access to be useful while respecting data boundaries, permissions, and company policies.

- Evaluate build vs. buy: Maintain a strong perspective on the AI tooling ecosystem, evaluate commercial tools, and recommend what π should adopt.

- Measure impact: Define success metrics for adoption, productivity, and satisfaction. Use feedback and data to understand what is working, what is not, and where to invest.

What We Hope You'll Bring

- Strong software engineering fundamentals and the ability to ship quickly.

- Deep excitement about AI tools and strong opinions about how they should be used.

- Hands-on fluency with AI coding workflows and modern LLM-based tools.

- Technical flexibility: ability to build backend services, internal tools, integrations, automation, and user interfaces.

- Strong product judgment and taste for developer experience and internal tooling.

- High empathy and excitement to work across engineering, research, operations, recruiting, and other teams.

- Ability to learn unfamiliar systems quickly and operate across many technical domains.

- Good judgment around security, permissions, data access, and safe tool rollout.

- Clear communication, documentation, and teaching ability.

- Comfort driving adoption, not just writing code.

Bonus Points

- Experience building developer tools, agents, or automation platforms.

- Experience building internal tools specifically for research, robotics, or operationally-intensive problems.

- Experience with our specific stack: React, TypeScript, Python, Postgres, ClickHouse, GCP, and Kubernetes.