Senior Forward Deployed Engineer, Handshake AI Enterprise

Handshake•San Francisco, CA

3d•Onsite

About The Position

As a Senior Forward Deployed Engineer at Handshake AI Enterprise, you'll embed directly inside enterprise customer environments as part of a small, expert team. You'll learn each customer's business deeply — their workflows, their bottlenecks, where the real leverage is — and translate that understanding into production-grade AI agents that measurably change how they operate. This isn't an implementation or consulting role. You'll own the full stack: defining the solution, building and deploying agents, designing evals, and iterating until performance actually moves. Over time, you'll become a genuine domain expert in your customer's vertical — and use that expertise to build AI that keeps getting better. If you've built AI that works in the real world and want to do it again at much higher stakes — with better data, closer lab relationships, and a founding team that moves fast — this is the role.

Requirements

5–6+ years of engineering experience, full-stack with strong backend depth
Real-world experience building and shipping AI applications in production — not just fine-tuning or prototyping
Strong understanding of agent architectures, evals, and how to measure and systematically improve AI performance
Comfort working directly with enterprise customers in ambiguous, high-stakes environments
Strong systems thinking — you can zoom out to the business problem and zoom in to the technical solution without losing either thread
High ownership mentality — you care about outcomes, not just outputs
Strong communication skills — you'll be in the room with senior stakeholders regularly and need to earn their trust quickly

Nice To Haves

Background at a company with a forward deployment or field engineering model
Experience building multi-agent systems in production
Domain expertise in a specific enterprise vertical (recruiting, finance, ops, legal, etc.)
Familiarity with enterprise security requirements, VPC deployments, or on-prem configurations
History of building evals infrastructure or AI quality measurement tooling

Responsibilities

Embed directly with enterprise customers as part of a small deployment team, developing deep understanding of their business and workflows
Define AI-driven solutions based on real business needs — not theoretical use cases
Build and deploy production-grade agents tailored to specific customer use cases, owning end-to-end delivery
Design and run evals to measure agent performance; iterate and hill-climb until results move
Become a domain expert in your customer's vertical and use that expertise to build better AI over time
Work closely with Product and Platform engineers on your team to ship cohesive, reliable solutions
Establish engineering best practices, agent patterns, and reusable frameworks that help the forward-deployed team scale output without sacrificing quality
Surface insights from deployments that inform Handshake's platform and product roadmap