Define and drive the team's goals and technical direction for AI Solutions, delivering measurable business impact for priority product verticals and making the larger organization more efficient through LLM-based automation
• Effectively communicate complex AI systems (LLM post-training, multi-modal content understanding, agent harnesses and agentic platforms) and their impact to engineers, partners, and leadership
• Track industry and company-wide AI trends to assess and develop new techniques, fostering innovation, rapid prototyping, and technical rigor in pursuit of state-of-the-art performance
• Partner with organizations and cross-functional leaders, and mentor applied researchers and engineers, to raise the performance of the team and organization, and ensure end-to-end delivery of high-quality, scalable AI/ML systems
• Lead as an AI-native engineer: use AI agents and tools daily in how you design, build, and ship, and grow those ways of working across the team
• Identify high-leverage opportunities for LLM-based automation across Meta's product portfolio and influence cross-functional partners on staffing and prioritization
• Lead the long-term technical vision and roadmap for large, cross-company AI efforts, aligned with the team's mission to deliver cost-efficient, performant AI models
• Collect and synthesize requirements from partner teams into an effective roadmap for AI solutions: product enablement for rapid integration, agentic solutions that move from rigid workflows to autonomous problem-solving, and AI quality scaling for specific, complex problems
• Identify and resolve performance, cost, and scalability issues in AI systems, and drive large efforts to reduce technical debt
Minimum Qualifications
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 12+ years of programming experience in a relevant language, or 8+ years of programming experience in a relevant language with a PhD
• Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience
• Experience influencing and advising executive stakeholders across organizations
• Experience utilizing data and analysis to explain technical problems and provide detailed feedback and solutions
• Experience collaborating with cross-functional partners (e.g., product, design, data science) to define requirements and deliver integrated solutions
• Proven track record of planning multi-year roadmaps in which short-term projects lead to the long-term mission
• Experience driving large cross-functional engineering efforts across multiple teams or organizations
Preferred Qualifications
• Knowledge of NLP techniques, including text preprocessing, tokenization, and sentiment analysis
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Understanding of information retrieval concepts, such as indexing, querying, and ranking
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated experience with data structures and algorithms, including graph theory and optimization techniques
• Track record of designing and implementing agentive AI systems and frameworks (e.g., agent harnesses, multi-agent orchestration) in production
• Master's degree or a PhD in Computer Science or a related technical field
• Experience with frameworks like TensorFlow, PyTorch, or Scikit-learn
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience building large-scale, multi-stage reasoning systems in high-QPS production environments, including LLM post-training (e.g., reinforcement learning, fine-tuning) to improve quality and cost
