Confirmed on the employer's own hiring board on Nov 2, 2026. First seen by Alion on Oct 22, 2026. Meta scores B on the Alion truth index.
Responsibilities
Design and conduct original machine learning research in areas such as deep learning, generative models, representation learning, or optimization Develop, implement, and evaluate novel ML algorithms and model architectures using large-scale training infrastructure Translate research findings into production-ready systems in collaboration with engineering and product teams Run controlled experiments to test research hypotheses and make data-driven decisions about model design and training strategies Contribute to the preparation and submission of research papers to peer-reviewed venues and conferences Participate in code reviews and maintain high standards for research code quality, reproducibility, and documentation Instrument experiments with appropriate logging, monitoring, and evaluation metrics to understand model behavior at scale Collaborate with cross-functional partners including data scientists, engineers, and product teams to identify high-impact research directions Identify and address performance bottlenecks in model training and inference pipelines Provide technical feedback to peers through design reviews, experiment critiques, and research discussions
Qualifications
Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta 2+ years of experience conducting machine learning research, including developing and evaluating novel ML models or algorithms Experience implementing machine learning systems in Python and at least one deep learning framework such as PyTorch Experience designing and running experiments to validate research hypotheses, including statistical analysis of results Experience communicating research findings in written form, such as technical reports, papers, or internal documentation Experience training or fine-tuning large-scale models on distributed compute infrastructure Familiarity with research areas such as self-supervised learning, reinforcement learning or efficient model architectures Experience bridging research prototypes and production ML systems in a collaborative engineering environment Publication record at peer-reviewed machine learning venues such as NeurIPS, ICML, ICLR, CVPR, or equivalent

