About the Role:We're hiring a Forward Deployed Engineer (FDE): a deeply technical builder and technical leader who thrives at the intersection of engineering, product, and real customer problems.
You'll work shoulder-to-shoulder with customers and internal product and engineering teams to design, ship, and iterate on mission-critical integrations and AI-driven systems that run in production. You'll write real code, own complex deployments end-to-end, and help shape how these systems are built across Middesk. Forward Deployed Engineers close the gap between product intent and real-world usage-acting as a tight feedback loop that shapes both how our product is used and what it becomes.
You will act as a technical leader, helping set direction, influence architecture, and establish best practices for how we deploy and operate systems in the real world.
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:Own Technical Direction for Deployments
- Lead architecture and technical design for complex, customer-facing systems and integrations.
- Make and communicate clear trade-offs across speed, reliability, and scalability.
- Identify systemic risks and drive improvements across deployments, not just one-off solutions.
- Establish patterns and standards that other engineers can build on-helping define what "great" looks like for Forward Deployed Engineering at Middesk.
Build & Deploy Real Systems
- Partner deeply with strategic customers to understand their architecture, workflows, and constraints.
- Design, build, and deploy production-grade integrations using Middesk APIs, data pipelines, and AI workflows.
- Own deployments end-to-end: from architecture and implementation to rollout, monitoring, and iteration. Deployment quality matters-we operate in the critical path of customer systems.
- Solve ambiguous, high-impact problems that don't yet have established playbooks.
AI, Agents & Evaluation
- Build and operate LLM- and agent-based systems in real customer workflows.
- Design and maintain evaluation frameworks (evals) to measure quality, reliability, and correctness of LLM outputs and agent behavior.
- Create test harnesses, metrics, and feedback loops to continuously improve AI-driven systems.
- Feel comfortable running multiple coding agents or automated workflows in parallel, debugging failures, and reasoning about system behavior.
Influence Product & Engineering Direction
- Translate deployment learnings into reusable abstractions, internal tooling, and product improvements.
- Influence roadmap priorities by surfacing recurring patterns, technical gaps, and opportunities for leverage.
- Participate in architecture discussions and contribute to org-wide technical decisions.
- We believe engineering velocity is only valuable if it delivers customer value-your role is to make sure it does.
Lead & Raise the Bar
- Lead through influence, not authority-setting a high standard for engineering quality and pragmatism in a high-ownership, high-trust environment.
- Guide engineers through design reviews, code reviews, and hands-on collaboration.
- Enjoy real autonomy, direct influence on product direction, and deep technical problems with real-world impact.
- Work with a team that values thoughtful engineering over performative process.
What We're Looking For:Core Requirements
- 6+ years of experience building and shipping software in production environments.
- Demonstrated experience acting as a technical lead or domain owner for a team or major system.
- Strong proficiency in Python and at least one additional language (TypeScript, Go, etc.).
- Experience designing and operating distributed systems, APIs, and cloud-native infrastructure.
- Comfort operating across the stack: backend systems, integrations, infrastructure, and tooling.
- Strong debugging instincts and an ownership mindset-you care deeply about failure modes, not just happy paths.
- Strong communication skills-able to articulate trade-offs clearly to engineers, product, and non-technical stakeholders.
AI / LLM Experience (Strong Plus)
- Hands-on experience building or deploying LLM-powered or agent-based systems.
- Familiarity with LLM evaluation techniques (evals) and performance measurement.
- Experience orchestrating automated workflows, agents, or developer tooling powered by AI.
- Clear understanding of failure modes and tradeoffs in applied AI systems.
Customer & Domain Experience
- Experience working in startup environments with high ambiguity and rapid iteration.
- Background in fintech, compliance, or regulated systems where correctness and data quality matter.
- Comfortable working directly with customers on complex technical problems.
Nice-To-Have:- Former founder or early startup engineer.
- Experience with identity systems, risk/fraud, or workflow automation.
- Familiarity with observability, reliability engineering, or system safety practices.
