Forward Deployed Engineer

Excerpt from the original posting

Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.
About the role 
You embed directly with customer teams as the engineer on the ground: own an outcome, ship a production-grade SpaceXAI system that still runs after you leave, and become the critical technical feedback loop from the field back to product engineering.
The work is the project. One week you might take a messy business workflow and put a production agent on it: pick the use case, stand up the architecture, wire it into the customer’s auth, data, and tools, then harden it with evals until the team actually uses it. Another week you might live in a customer’s repo and delivery system and ship an agent-powered path for a migration, a PR review loop, or an incident-to-fix flow, behind tests, review, and rollout rather than in a chat sidebar. Either way, you measure what matters, leave the customer able to operate it, and bring the pattern back as another use case for SpaceXAI.
This is not a demo role. You own the work end to end, from the first discovery call through launch, iteration, and post-launch support, and you are responsible for systems that work in the real world.

What you’ll do 
- Lead discovery with the customer: orient in their codebases and workflows, find the real bottleneck and the use case worth building, and define clear success metrics

- Design and ship production agents, AI applications, and coding-agent workflows on Grok and our models, on live business processes and inside the customer’s engineering org

- Get a first version live in days, then harden it over weeks with rollout, monitoring, and iteration from real usage

- Own production quality: tracing, evals, debugging model or delivery failures, and latency and cost tradeoffs

- Build the systems around the model (tools, MCP servers, retrieval, rules, skills, CI gates, and evals) and integrate them into the customer’s auth, data, workflow, and delivery systems; decide when prompting, architecture, data, process, or a model change is the right lever

- Measure the outcome that matters (revenue, cost, hours saved, error rate, cycle time, escaped defects), not seat adoption

- Work dire…

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