Forward Deployed Engineer

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.

The Role As a Forward Deployed Engineer at Laurel, you’ll work directly with customers to design, build, and deploy AI-powered systems that solve real operational problems. You’ll operate at the intersection of engineering, product, and customer delivery — embedding deeply with customer teams, understanding their workflows firsthand, and shipping production-grade systems that create measurable impact. This is not a solutions architect role. It is not post-sales support. And it is not lightweight integration work.You will write production code, own technical outcomes end-to-end, and help shape the core Laurel platform through what you learn in the field. The best FDEs are highly technical product engineers who also happen to be exceptional at navigating ambiguity, communicating with stakeholders, and turning incomplete information into working systems. You should be excited by questions like: How do we turn a messy operational workflow into a reliable AI system? What breaks when LLMs meet real enterprise data? How do we build infrastructure that works across dozens of customer environments? When should something become productized versus remain customer-specific? How do we go from “interesting prototype” to trusted production workflow in days, not months? What You’ll Do Work directly with customers and Laureates to understand operational workflows, constraints, and technical environments Design and implement production-grade solutions across backend services, orchestration layers, integrations, and internal tooling Write high-quality code across the stack — typically TypeScript, APIs, data pipelines, and cloud infrastructure Own deployments end-to-end: discovery, architecture, implementation, testing, rollout, monitoring, and iteration Debug complex issues across customer systems, infrastructure, authentication, APIs, and data pipelines Collaborate closely with Product and Core Engineering to turn field learnings into reusable platform capabilities Rapidly prototype new functionality while maintaining strong engineering judgment around scalability and maintainability Translate ambiguous customer needs into clear technical plans and execution paths Help customers operationalize AI safely and effectively in real production environments Help accelerate the implementation team’s efforts to reduce time to impact for customers. What We’re Looking For We’re looking for engineers who combine strong technical fundamentals with exceptional ownership and execution. You may be a fit if you: Have 4+ years of experience building production software systems Are comfortable moving across backend, infrastructure, integrations, and customer environments Can independently architect and ship complex systems with minimal oversight Have experience with APIs, distributed systems, cloud infrastructure, and data-intensive applications Have worked with LLMs, AI agents, retrieval systems, orchestration frameworks, or applied AI systems in production Thrive in ambiguous environments where