Job description

Meta is seeking experienced engineers to join AI Solutions and Automation + Support (ASA+S), where we apply state-of-the-art AI and autonomous agents to the products and support experiences used by billions of people. We build agents that resolve real problems for users, creators, and businesses, along with the platforms, evaluation systems, and model infrastructure that let those agents ship safely and at scale. This is applied AI where research meets production: you'll build and optimize LLM and agentic systems, design the data and evaluation foundations that make them trustworthy, and take frontier techniques from prototype to production. It's deliberately high-leverage work; what we prove once becomes a platform that other teams build on. If you want to work on genuinely novel agent problems and see your work reach real users quickly, this is the right team for you. 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 organization 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 12+ years of programming experience in a relevant language OR 8+ years experience with a PhD Bachelor's degree in Computer Science, Computer Engineering, 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 communicating and working across functions to drive solutions Proven track record of planning multi-year roadmaps in which short-term projects ladder to the long-term mission Experience driving large cross-functional/industry-wide engineering efforts Experience with frameworks like 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 Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, 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 adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) 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 Track record of designing and implementing agentive AI systems and frameworks (e.g., agent harnesses, multi-agent orchestration) in production

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aillmml

Platform: meta
Effort: medium

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