前移部署工程师 - 欧洲、中东和非洲
Forward Deployed Engineer - EMEA
我们的使命是自动化编码。我们旅程的第一步是通过创新研究、设计和工程的结合，打造最适合专业程序员的工具。我们的组织结构非常扁平，团队小而人才密集。我们特别喜欢那些追求真理、充满热情且富有创造力的人。我们享受激烈的辩论、疯狂的想法以及持续交付代码。
关于该职位
Forward Deployed Engineers 会直接嵌入客户工程团队，交付可衡量提升他们软件构建方式的生产级 Cursor 工作流。
你将找出真正拖慢团队效率的问题，设计实用的解决方案，并从首次发现会议到发布、迭代和生产后支持，全程负责。这不是一个演示角色。你负责的是在真实世界中运行的系统。
你将做的事情
- 与客户工程师进行发现工作，识别真正的瓶颈并定义明确的成功指标
- 设计并构建基于 Cursor 的工作流（例如大规模重构、迁移、PR 审核循环、事件 → 修复流程、规格 → 实现系统）
- 在几天内交付第一个快速版本，然后通过适当的发布计划、监控和基于实际使用的迭代，将其强化为可靠的产品
- 负责生产质量：追踪、评估、指标、调试模型行为、延迟/成本权衡和故障模式
- 直接与 Staff+/Platform/Eng 领导合作——深入代码的同时也能清晰地沟通权衡和结果
- 将你构建的内容转化为可复用的模式，并将改进推回到核心 Cursor 产品中
你可能适合这个职位如果
- 你在模糊环境中表现出色，能够将模糊的问题转化为明确的范围和交付的系统
- 你已经在生产环境中构建并负责过 AI 原生的工作流，而不仅仅是原型，并且调试过真实的模型故障
- 你习惯主导发现对话，并对客户成果负责
- 你负责端到端的开发。如果需要前端、后端、基础设施或提示迭代，你都会负责
- 你之前处理过生产可靠性问题（指标、警报、安全发布、事件响应）
- 你擅长 Python 和 TypeScript/JavaScript，并能快速上手新的技术栈
查看英文原文
SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.
SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.
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 directly with Staff+, platform, and domain leaders, going deep in their systems while communicating clearly about tradeoffs and results
- Leave the customer team able to run what you built, and turn what you learn into reusable patterns and improvements to SpaceXAI products
YOU MAY BE A FIT IF
REQUIRED
- You have 5+ years of experience in software engineering, machine learning engineering, or data science
- You write and review production code (Python, JavaScript/TypeScript; other languages welcome)
- You’ve owned a customer or operator outcome, not just a slide or a prototype, and can turn a fuzzy problem into a scoped, shipped system
- You’ve built and owned AI-native workflows or agents in production, and debugged real production failures (model, tool, data, pipeline, or delivery path)
- You’ve handled production reliability: metrics, alerts, safe rollouts, incident response
- You build end to end: frontend, backend, infra, and prompt iteration
- You thrive in ambiguity and don’t need a complete spec before you start building
- You can talk to working engineers and to a VP
NICE TO HAVE
- 2+ years in a customer-facing role, leading discovery conversations and being accountable for outcomes for external stakeholders
- Hands-on depth in several of: agents and tool-calling, eval harnesses, retrieval and context systems, CI/CD or developer platforms, and AI-native coding workflows in a real repo
- Experience as a forward-deployed, solutions, or platform engineer embedded with customers, or as a founding engineer who owned an AI product end to end