Responsibilities
Develop highly scalable algorithms based on state-of-the-art machine learning and neural network methodologies
• Suggest, collect, and synthesize requirements and create effective feature roadmap
• Code deliverables in tandem with the engineering team
• Adapt standard machine learning methods to best exploit modern parallel environments (e.g., distributed clusters, multicore SMP, and GPU)
• distributed clusters, multicore SMP, and GPU)
• Perform specific responsibilities which vary by team
• Research, design, and develop new algorithms and techniques to improve the efficiency and performance of Meta's platforms
• Gather data for machine-learning training; train new ranking models and run experiments
• Identify potential improvements in Meta's software and technology products
• Research and present effects of current engineering efforts on Meta's market standing from an economic and game-theoretic point of view
• Develop highly scalable classifiers and tools leveraging machine learning, data regression, and rules based models
Minimum Qualifications
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Experience with research and/or work in machine learning, NLP, reinforcement learning, deep learning, recommendation systems, pattern recognition, signal processing, data mining, artificial intelligence, information retrieval, or computer vision
• Experience coding in one or more of the following: Java, C++, Perl, PHP, or Python
Preferred Qualifications
• PhD in Computer Science, Computer Engineering, or relevant technical field
• First author publications experience at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, and ACL)
• Experience working and communicating cross functionally in a team environment
• Experience with large scale model training, implementing algorithms, and evaluating speech-based systems
• 3+ years experience as an Applied AI Research Scientist or an Applied AI Research Engineer
• Experience taking ideas from research to production
• Experience with developing scalable machine learning models in at least one of the following areas: Crawling, indexing, extraction, retrieval, ranking, recommendations, measurement, tooling, evaluation, query understanding, planning, vector databases, or embeddings
• Experience evaluating alternative technical solutions by analyzing tradeoffs in performance, cost, and maintainability to recommend a path forward
