Senior Staff AI Engineer, Faraday Future

• Fluency in both English and Chinese is required
• Education: Bachelor's degree or above in Computer Science, Electrical Engineering, or a related technical discipline
• Experience: 7-10 years of software engineering experience, with meaningful hands-on work in AI/ML systems, AI agents, or AI-enabled tools and products
• AI Engineering: Demonstrated ability to design and ship AI-powered products — not just calling APIs, but building orchestration logic, prompt systems, memory layers, and tool-integration chains
• LLM Fundamentals: Solid understanding of LLM capabilities and boundaries — context windows, prompting patterns (System Prompt, CoT, Few-shot), model selection, and cost/quality trade-offs
• AI-Assisted Development: Daily, proficient user of Claude Code, Cursor, or equivalent AI coding tools. Able to clearly demonstrate how these tools multiply your engineering output
• Business Sense: Comfortable working alongside non-technical teams (marketing, content, product, operations). Strong ability to translate ambiguous business needs into clean engineering solutions and to prioritize based on real-world impact
• Delivery Track Record: Has independently shipped at least one complete product, internal platform, or meaningful open-source contribution from idea to production — evidence of end-to-end delivery capability
• Fluency in both English and Chinese due to internal support for Global Team
• Ability to work on-site in Los Angeles, CA

Desirable

• The Senior Staff AI Engineer, Generative AI Platforms & Workflows will lead the engineering of AI-native platforms and automation systems that power content production, creative operations, and cross-functional workflows inside FF's Visual Marketing and broader creative organization
• This role sits at the intersection of AI engineering, agent orchestration, and business workflow automation, with the goal of transforming how FF's marketing, content, and operations teams work — turning manual, time-consuming processes into scalable, AI-powered systems
• You will design and ship AI agents, tool integrations, and production-grade automation that enable a small team to deliver with the output of a much larger one
• This is a hands-on engineering role
• You will architect and build the systems yourself — multi-agent orchestration, memory and knowledge layers, model routing, tool integration, and the user-facing interfaces that sit on top
• You will also contribute to FF's broader EAI strategy by helping develop internal AI platforms that the Company plans to roll out across its marketing, creative, and operations functions
• This position reports to the Head of Visual Marketing and works cross-functionally with marketing, content, product, and engineering teams to align AI-powered platforms with business objectives and measurable outcomes
• AI Workflow Engineering: Build AI-powered workflow systems that help marketing, content, and operations teams automate complex creative and business tasks — from asset generation and campaign operations to research, review, and distribution pipelines
• Agent Platform Development: Design and implement multi-agent orchestration infrastructure — state machines, supervisor-executor patterns, task scheduling, memory and knowledge systems, and failure recovery — so that non-technical teams can configure and run their own AI teams
• Generative Content Pipelines: Architect end-to-end generative AI pipelines spanning image, video, copy, and multimodal asset generation to support FF's global brand marketing, product launches, and visual storytelling initiatives
• Tooling & Integration Layer: Integrate AI tools, MCP-based plugins, and third-party services (OpenRouter, Composio, Claude Code, Cursor, Midjourney, Runway, ComfyUI, and similar) into production-grade workflows that can be safely operated by business users
• AI-Native Engineering Practices: Use AI coding tools (Claude Code, Cursor, and equivalents) as your primary development method. Establish engineering workflows, prompts, and patterns that maximize one-engineer throughput through AI-assisted development
• Business-Aligned Delivery: Work directly with marketing, content, product, and operations stakeholders to understand pain points, translate messy business requirements into clean technical solutions, and measure impact on time-to-ship, content quality, and operational cost
• Platform & SOP Thinking: Design for reuse and scale — build shared components, asset libraries, SOPs, and self-serve tooling so creative and business teams can operate AI systems independently, without engineering bottlenecks
• Innovation & Experimentation: Continuously evaluate emerging models, agent frameworks, and creative AI tools. Prototype rapidly, validate against real business workflows, and bring proven technology into production