Forward Deployed Engineer, Enterprise AI
Meta, Menlo Park, California +1
About the job
We help global enterprise partners integrate Meta's foundation models and AI tools directly into their core products and infrastructure. We are hiring an experienced Staff Forward Deployed Engineer to act as the primary technical bridge between Meta's internal engineering teams and our enterprise clients. You will lead the architecture and rollout of large-scale AI systems, working side-by-side with engineering leaders at other companies to adapt our AI platform to their specific environments and constraints. Crucially, you’ll take the friction you encounter in the field—like optimizing inference speeds, resolving interoperability issues, and connecting complex external data sources—and turn it into concrete technical proposals. Your hands-on experience will directly inform and shape the roadmap for Meta's core AI tools and ecosystem. If you want autonomy and the chance to lead complex technical deployments that define how the enterprise uses AI, we encourage you to apply.
Responsibilities
Architect Systems: Lead the technical design for major enterprise AI deployments. Ensure the solutions can scale, remain secure, and adhere to strict partner compliance requirements. Build Integrations: Design reliable connections between Meta's AI platforms and client infrastructure, including CRMs, data warehouses, and custom APIs. Drive the Roadmap: Serve as the definitive technical link between field deployments and internal platform teams. Synthesize real-world deployment challenges into strategic decisions that shape Meta's core AI products. Establish Standards: Define deployment patterns, build tooling, and set engineering standards that reduce technical debt and speed up future execution across the team. Lead and Mentor: Set technical direction, drive major cross-functional initiatives, and provide deep technical mentorship to uplevel the team and guide new engineers. Optimize at Scale: Identify and resolve complex performance, latency, and scalability bottlenecks to ensure reliable performance in live production environments.