Software Engineer (Technical Leadership) - AI Solutions and Automation + Support

Meta•Bellevue, WA

3d•$219,000 - $301,000

About The Position

Meta is seeking experienced engineers to join AI Solutions and Automation + Support (ASA+S), applying state-of-the-art AI and autonomous agents to products and support experiences used by billions of people. The team builds agents that resolve real problems for users, creators, and businesses, as well as the platforms, evaluation systems, and model infrastructure that enable safe, scalable deployment. The work combines applied AI research and production engineering, including building and optimizing LLM and agentic systems, developing data and evaluation foundations, and taking new techniques from prototype to production. The role drives technical direction and delivery for AI solutions across priority product verticals and company-wide efforts. It involves developing roadmaps, identifying automation opportunities, improving AI system performance and cost, and working with organizational leaders, partner teams, applied researchers, and engineers to deliver scalable systems. The engineer is expected to use AI agents and tools in day-to-day engineering work and help expand those practices across the team.

Requirements

12+ years of programming experience in a relevant language, or 8+ years of experience 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 using data and analysis to explain technical problems and provide detailed feedback and solutions.
Experience communicating and working across functions to drive solutions.
Proven track record planning multi-year roadmaps in which short-term projects support a long-term mission.
Experience driving large cross-functional or industry-wide engineering efforts.

Nice To Haves

Experience with frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Demonstrated experience with data structures and algorithms, including graph theory and optimization techniques.
Knowledge of NLP techniques, including text preprocessing, tokenization, and sentiment analysis.
Ongoing AI skill development, such as prompt/context engineering and agent orchestration, and staying current with emerging AI technologies.
Ability to integrate AI tools to optimize or redesign workflows and drive measurable impact, such as efficiency gains or quality improvements.
Master's degree or PhD in Computer Science or a related technical field.
Understanding of information retrieval concepts, such as indexing, querying, and ranking.
Experience implementing responsible, ethical AI practices, such as risk assessment, bias mitigation, and quality and accuracy reviews.
Experience building large-scale, multi-stage reasoning systems in high-QPS production environments, including LLM post-training such as reinforcement learning or fine-tuning to improve quality and cost.
Track record designing and implementing production agentive AI systems and frameworks, such as agent harnesses and multi-agent orchestration.

Responsibilities

Drive the team's goals and technical direction for AI Solutions, delivering measurable business impact for priority product verticals and improving organizational efficiency through LLM-based automation.
Communicate complex AI systems—including LLM post-training, multimodal content understanding, agent harnesses, and agentic platforms—and their impact to engineers, partners, and leadership.
Track industry and company-wide AI trends, assess and develop new techniques, and foster innovation, rapid prototyping, and technical rigor.
Partner with organizational and cross-functional leaders, mentor applied researchers and engineers, and raise team performance while ensuring end-to-end delivery of high-quality, scalable AI/ML systems.
Use AI agents and tools in daily design, development, and delivery, and grow AI-native ways of working across the team.
Identify high-leverage opportunities for LLM-based automation across Meta's product portfolio and influence cross-functional staffing and prioritization.
Lead the long-term technical vision and roadmap for large, cross-company AI efforts aligned with cost-efficient, performant AI models.
Collect and synthesize partner-team requirements into an AI solutions roadmap covering product enablement, agentic solutions, and AI quality scaling for complex problems.
Identify and resolve AI system performance, cost, and scalability issues, and lead efforts to reduce technical debt.