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Laurel is an AI Time platform serving major professional services firms (EY, Aprio, Crowell & Moring, Frost Brown Todd) that automates work time capture and connects time data to business outcomes. The company processes over 1 billion work activities annually and is backed by Google Ventures, IVP, Anthos, Upfront Ventures, and notable individuals including Marc Benioff and Alexis Ohanian.
As a Senior Developer Productivity Engineer, you will design, build, and deploy AI-powered systems that multiply the effectiveness of Laurel's engineering teams across Frontend, Backend, AI/ML, Infrastructure, and FDEs. You'll learn engineering workflows, identify pain points, and improve processes to increase throughput and quality. You'll evaluate cutting-edge software development tools from frontier labs, infrastructure providers, and hyperscalers, determining what's worth adopting and how to integrate it into the organization.
Key responsibilities include:
- Work directly with leadership to determine what to measure, build, and buy
- Interface with engineering teams to understand how work happens at Laurel
- Partner with teams to teach new tools, collect feedback, and iterate
- Design systems consumable by both human and agent users
- Create new CI/CD pipelines for extreme throughput and agent interactivity
- Work on multiple application platforms for external products and internal usage
- Own distribution and adoption, making tools people want to use
- Collect feedback through word of mouth and technical telemetry
- Stay current with industry trends in the fastest-moving sector of technology
You'll work in an ambiguous, fast-moving environment where requirements evolve quickly. Success means earning deep trust with engineers, creating powerful extensible tools, minimizing learning curves, and operating with urgency while maintaining strong engineering judgment.
REQUIREMENTS:
- 4+ years of experience in Platform, Infrastructure, DevEx, or DevOps roles
- Can independently architect and ship complex systems with minimal oversight
- Experience with APIs, distributed systems, cloud infrastructure, and data-intensive applications
- Experience building with agent primitives: Skills / MCPs / Hooks, SDKs, multi-agent systems, routing, context and cost management
- Experience with configuration management tools like Nix / NixOS, homebrew, or MDM tools
- Thrive in ambiguous environments where requirements evolve quickly
- Clear communication with both engineers and non-technical stakeholders
- Care deeply about speed, iteration, and customer impact
NON-NEGOTIABLE REQUIREMENTS:
- Experience with Kubernetes and AWS
- Development experience with Terraform, Go, and Typescript
- Experience working in startup environments
- Significant experience with Linux administration
- Familiarity with Git
- Experience with regular engineering oncall rotation
- Experience with CI tooling/platforms
NICE TO HAVES:
- Startup experience or early-stage product building
- Experience deploying AI systems into enterprise environments
- Strong systems engineering or infrastructure background
- Experience integrating with third-party enterprise systems and APIs
- Familiarity with authentication systems, security constraints, and enterprise IT environments
- Experience debugging production issues in unfamiliar codebases
- History of shipping quickly under real-world constraints
- Comfort balancing rapid iteration with long-term engineering quality
- Experience with specific tech stack: Argo, cdk8s, Open Telemetry & Observe, MongoDB, Postgresql, Spacelift, CircleCI, GitHub Actions
