Forward Deployed Engineer

Excerpt from the original posting

About Middesk: 
Middesk is building the data and intelligence infrastructure that helps businesses work together with confidence. We started by creating a comprehensive platform for understanding businesses, bringing together authoritative and proprietary data to help customers verify business identities, onboard customers faster, and manage risk throughout the customer lifecycle.
Today, Middesk is used by more than 700 banks and fintechs, and in 2025 we verified more than 7 million companies. We've also expanded beyond business verification to help companies form, register, manage, and maintain their businesses, supporting more than 50,000 companies in setting up over 100,000 accounts required to hire employees, run payroll, and stay compliant.
Middesk came out of Y Combinator, and is backed by Sequoia Capital, Accel, Insight Partners, and Canapi. We're proud to be named on the Forbes Fintech 50 and Best Startup Employers lists. 

About the Role: 
We're hiring a Forward Deployed Engineer (FDE) who thrives at the intersection of engineering, product, and real customer problems.
You'll work directly with customers and internal teams to design, build, and deploy production-grade integrations and AI-driven systems. You'll own complex technical problems end-to-end, from understanding customer needs to shipping reliable systems in production. You'll also turn what you learn in the field into improvements that shape Middesk's product and engineering direction.
This role is ideal for a recovering founder, a former core engineer who misses direct customer impact, or someone who thrives on ownership and wants to build real systems where the feedback loop is immediate.

What You’ll Do: 
- Build and deploy real systems: Partner with customers to design, implement, and operate production-grade integrations using Middesk APIs, data pipelines, and AI workflows.

- Own deployments end-to-end: Drive technical design, implementation, rollout, and iteration, making pragmatic trade-offs across speed, reliability, and scalability.

- Build AI-powered workflows: Develop LLM- and agent-based systems, including evals, test harnesses, and feedback loops to measure and improve quality.

- Solve ambiguous problems: Tackle complex customer challenges without established playbooks, working across backend systems, infrastructure, and tooling.

- Shape the product: Turn customer learnings into reusable solutions, internal tooling, and product improvements that benefit the broader custo…

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