Back to search
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
- 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 working and communicating cross functionally in a team environment
- PhD in Computer Science, Computer Engineering, or relevant technical field
- 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
- Experience taking ideas from research to production
- First author publications experience at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, and ACL)
$219,000/year to $301,000/year + bonus + equity + benefits
Learn more about this Employer on their Career Site
