Forward Deployed Engineer, Enterprise AI

Meta; Menlo Park, Seattle (CA, WA); On-site; USD 230,000 - 320,000; Full time

Job summary

Meta is seeking a Staff Forward Deployed Engineer to serve as the primary technical bridge between Meta's internal teams and enterprise clients in Menlo Park, CA. You will lead architecture and rollout of large-scale AI systems, working side-by-side with engineering leaders to adapt our AI platform to client environments and constraints.

You will take field friction—like optimizing inference speeds, interoperability issues, and external data connections—and turn it into concrete technical

Qualifications

• Lead architecture and rollout of large-scale AI systems for enterprise partners.
• Bridge between Meta's internal teams and enterprise clients to tailor AI platforms.

Responsibilities

• Architect and deploy large-scale software systems used in GenAI infrastructure.
• Drive cross-functional engagement with enterprise clients and engineering leaders.
• Translate field feedback into robust product and platform roadmaps.
• Optimize inference speeds and interoperability across environments.

Skills

Large-scale system design

GenAI infrastructure scaling

Technical leadership

Customer-facing engagements

Prompt/context engineering

Education

Bachelor's degree in CS/CE or equivalent

PhD in a relevant field

Tools

AWS

GCP

ML frameworks

Job description

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.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Experience utilizing data and analysis to explain technical problems and provide detailed feedback and solutions
• Demonstrated experience driving change within an organization and leading complex technical projects
• 8+ years of programming experience in a relevant language OR 4+ years experience with a PhD
• Experience architecting and deploying large-scale software systems, including taking complex AI applications from early prototypes to enterprise-ready production
• Experience architecting and scaling GenAI infrastructure across major cloud platforms (e.g., AWS, GCP), leveraging advanced cloud services (compute, distributed storage, ML frameworks) to optimize model performance, manage compute costs, and ensure enterprise-grade reliability
• Experience designing and implementing advanced AI testing systems (e.g., automated evaluation loops to proactively catch hallucinations, reasoning gaps, or safety issues in live products)
• Experience in regulated industries (financial services, insurance, healthcare) or navigating enterprise compliance requirements (SOC2, data residency, privacy frameworks)
• Demonstrated ability to lead technical engagements with external engineering teams (e.g., Forward Deployed Engineering, Solutions Architecture) and translate complex business constraints into robust system designs
• Demonstrated ability to act as a technical bridge between field deployments and internal engineering, using hands‑on customer insights to directly shape core product and platform roadmaps
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies