Senior Developer Productivity Engineer

Laurel

1d•$224,000 - $255,000•Remote

About The Position

Laurel is on a mission to return time. As the leading AI Time platform for professional services firms, we’re transforming how organizations capture, analyze, and optimize their most valuable resource: time. Our proprietary machine learning technology automates work time capture and connects time data to business outcomes, enabling firms to increase profitability, improve client delivery, and make data-driven strategic decisions. We serve many of the world's largest accounting and law firms, including EY, Aprio, Crowell & Moring, and Frost Brown Todd, and process over 1 billion work activities annually that have never been collected and aggregated before Laurel’s AI Time platform. Our team comprises top talent in AI, product development, and engineering—innovative, humble, and forward-thinking professionals committed to redefining productivity in the knowledge economy. We're building solutions that empower workers to deliver twice the value in half the time, giving people more time to be creative and impactful. If you're passionate about transforming how people work and building a lasting company that explores the essence of time itself, we'd love to meet you. About the Role As a Productivity Engineer at Laurel, you’ll design, build, and deploy AI-powered systems that multiply the effectiveness of our Engineering teams. You’ll work with engineers in many departments - Frontend, Backend, AI/ML, Infrastructure, and FDEs - to learn their workflows, find pain points, and improve processes to increase throughput quality and quantity. You’ll be testing the latest software development offerings from frontier labs, infrastructure providers, hyperscalers, and neoclouds, to find out what’s worth adopting and how to fit it into our organization. Setting up the latest thing isn’t enough - you’ll need to understand tool capabilities, find the extensible edges, and build connective tissue. Given the high pace of this field, you can expect to work on a high volume of projects that get completed, released, and then re-architected a few months later. You should be excited by questions like: How do we measure performance of increasingly complex, opaque, and non-deterministic systems? How do we decide what tradeoffs to make across cost, speed, and quality? What are the weak points at the edges of the best AI systems? What is AI likely to still be bad at next quarter, or next year? How do we measure the entire chain of value creation from our engineers to satisfied users? How do we accelerate every phase of that chain? How do we invest in projects we won’t need to completely scrap in 6 months, when the tide of frontier models continues to rise?

Requirements

4+ years of experience in Platform, Infrastructure, DevEx, or DevOps roles
Can independently architect and ship complex systems with minimal oversight
Have 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 using configuration management tools like Nix / NixOS, homebrew, or MDM tools.
Thrive in ambiguous environments where requirements evolve quickly
Can communicate clearly with both engineers and non-technical stakeholders
Care deeply about speed, iteration, and customer impact
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 taking part in a regular engineering oncall rotation
Experience with any 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 our specific technology stack: Argo, cdk8s, Open Telemetry & Observe, MongoDB and Postgresql, Spacelift, CircleCI and/or GitHub Actions

Responsibilities

Work directly with Laureates and leadership to know what to measure, build, and buy.
Interface with engineering teams to learn the many ways work happens at Laurel.
Partner with teams and ICs to teach new tools, collect feedback and iterate.
Designing systems which can be consumed equally by both human and agent users.
Create new CI/CD pipelines for extreme throughput and agent interactivity.
Work on multiple application platforms, both for our external product and internal usage.
Be responsible for distribution and adoption; learning how to make tools people want to use.
Collect feedback, through word of mouth and technical telemetry, to maintain situational awareness and iterate.
Stay on top of industry news and trends in the fastest moving sector of technology; hone opinionated instincts on what to pursue and what to ignore.