Research Scientist, Contextual AI and Multimodal Agents

Responsibilities: Define and drive a multi-year research strategy for core machine learning, connecting areas such as multimodal understanding, foundation models, efficient training and inference, and agentic reasoning into a coherent research portfolio Serve as a principal technical driver for critical, high-risk research bets with multi-year potential, carrying them from problem formulation through scientific validation, system demonstration, and productization Lead ambitious research initiatives across algorithm and system design, implementation, experimentation, evaluation, and transfer into reusable platforms or product capabilities Develop novel ML algorithms and architectures—including training, post-training, reinforcement-learning, and evaluation methods—that advance the state of the art in areas such as large-scale optimization, representation learning, and generalization Advance efficient training and inference through model compression, quantization, distillation, adaptive computation, and hardware-software co-design for on-device and large-scale deployment Establish shared datasets, benchmarks, evaluation frameworks, and research platforms that raise the quality bar across multiple teams and product areas Identify new product opportunities and build durable collaborations across Meta, academia, and industry to move research into widely useful technology Mentor researchers and engineers across teams, provide technical counsel on consequential decisions, and raise the scientific and engineering bar for the broader organization Publish influential research, contribute open-source software and datasets where appropriate, and strengthen Meta's position in the research community through talks, collaborations, and service Partner with legal, policy, and compliance teams to ensure that ML research and deployment practices uphold privacy, security, and responsible AI standards

Qualifications: Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Engineering, Electrical Engineering, or a related technical field 12+ years of research or applied-development experience in machine learning, artificial intelligence, or related areas Recognized authority in a relevant research area, with demonstrated breadth across adjacent areas such as multimodal learning, foundation models, efficient AI systems, or agentic AIExperience setting the technical direction for a research area and leading multiple complex initiatives whose methods, systems, or strategy were adopted beyond the immediate team Hands-on experience building and evaluating foundation-model systems, such as large language models or multimodal models using techniques like long-context reasoning, retrieval, or tool use Demonstrated success carrying research from inception into shipped product capabilities, widely adopted platforms, commercialized technology, or open-source systems with a meaningful user community Sustained record of influential research contributions demonstrated through publications at leading venues, widely adopted research artifacts, patents, or comparable impact on the field Experience building and leading durable cross-organizational collaborations, including partnerships with external research institutions or industry, and mentoring senior researchers or engineers beyond the immediate team Experience with Python and modern machine-learning frameworks such as PyTorch or JAX Reinforcement learning for reasoning, agent self-improvement, or adaptive behavior Experience driving ML efficiency and scalability improvements across large distributed training or inference systems, including model compression, quantization, distillation, and adaptive computation Research contributions that have introduced new paradigms or techniques subsequently adopted broadly within the ML community, such as novel architectures, training methods, or theoretical frameworks Implementation or deployment of ML systems on mobile, embedded, or other resource-constrained platforms Founding or growing research communities, open-source ecosystems, or strategic industry-academic collaborations Experience collaborating with privacy, security, or integrity teams to design ML systems that are robust, safe, and compliant with regulatory requirements Design of datasets, benchmarks, or evaluation methods for foundation models, multimodal understanding, or agent capability