Middesk

Forward Deployed Engineer

San Francisco · Exec

Sponsorship not specifiedDetected 76 days ago

TypeScriptPythonGoDistributed SystemsCode ReviewData EngineeringLLMsCommunication

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.
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.

Own Technical Direction for Deployments
Lead architecture and technical design for complex, customer-facing systems and integrations.
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
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.
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.

6+ years of experience building and shipping software in production environments.
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.

Former founder or early startup engineer.
Experience with identity systems, risk/fraud, or workflow automation.
Familiarity with observability, reliability engineering, or system safety practices.
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.
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.

Solve ambiguous, high-impact problems that don't yet have established playbooks.
Feel comfortable running multiple coding agents or automated workflows in parallel, debugging failures, and reasoning about system behavior.
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.
Enjoy real autonomy, direct influence on product direction, and deep technical problems with real-world impact.

Middesk makes it easier for businesses to work together.
Since 2018, we've been transforming business identity verification, replacing slow, manual processes with seamless access to complete, up-to-date data.
Our platform helps companies across industries confidently verify business identities, onboard customers faster, and reduce risk at every stage of the customer lifecycle.
Middesk came out of Y Combinator, is backed by Sequoia Capital and Accel Partners, and was recently named to Forbes Fintech 50 List.
Partner deeply with strategic customers to understand their architecture, workflows, and constraints.

This listing is sourced directly from Middesk's careers page and normalized into a canonical job model.