Job description

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 a minimum of 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 requirements evolve quickly - Can communicate clearly with both engineers and non-technical stakeholders - Care deeply about speed, iteration, and customer impact - Are excited to work directly with customers and own outcomes, not just tickets STRONG SIGNALS These are not requirements, but they tend to correlate strongly with success in this role: - 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 WHAT SUCCESS LOOKS LIKE Successful Forward Deployed Engineers at Laurel: - Earn deep trust with customer teams through technical credibility and execution - Move customers from pilot to production quickly - Create reusable technical patterns that improve the core platform - Identify the difference between one-off customization and durable product opportunity - Bring clarity to ambiguous problems - Operate with urgency while maintaining strong engineering judgment - Leave systems in a maintainable, scalable state after deployment WHY THIS ROLE MATTERS At Laurel, we believe the best enterprise software comes from engineers working directly with real customer problems — not from building in isolation. The insights generated in the field shape our roadmap, platform architecture, and product direction. This role is ideal for engineers who want to: - Build and ship constantly - Work on hard technical problems with real-world constraints - Develop strong product instincts - Operate with high ownership and autonomy - See the direct impact of their work in production environments If you’re energized by ambiguity, technical depth, customer context, and fast execution, we’d love to talk. WHY JOIN LAUREL: - To date, we've secured significant funding from renowned venture capitalists (Google Ventures, IVP, Anthos, Upfront Ventures), as well as notable individuals like Marc Benioff, Gokul Rajaram, Kevin Weil, and Alexis Ohanian - A smart, fun, collaborative, and inclusive team - Great employee benefits, including equity and 401K - Bi-annual, in-person company off-sites, in unique locations, to grow and share time with the team - An opportunity to perform at your best while growing, making a meaningful impact on the company's trajectory, and embodying our core values: understanding your "why," dancing in the rain, being your whole self, and sanctifying time We encourage diverse perspectives and rigorous thinkers who aren't afraid to challenge the status quo. Laurel is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. We are not able to support visa sponsorship or relocation assistance. If you think you'd be a good fit for this role, we encourage you to apply, even if you don’t perfectly match all the bullet points in the job description. At Laurel, we strive to create an inclusive culture that encourages people from all walks of life to bring their unique, diverse perspectives to work. Every day, we aim to build an environment that empowers us all to do the best work of our careers, and we can't wait to show you what we have to offer!

Requirements

Must-haves

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.
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.
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?
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.
We’re looking for engineers who combine strong technical fundamentals with exceptional ownership and execution.
Have a minimum of 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 requirements evolve quickly

Nice-to-haves

None flagged.

Application prep

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aimachine learninggotypescript

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

401k