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Meta’s MSL Airstore team is seeking a Software Engineer to lead the development of next-generation GenAI dataloading and storage systems. The ideal candidate will have experience in designing and scaling data infrastructure for AI/ML applications, with a focus on enabling efficient ingestion, transformation, data loading, and storage of massive datasets powering GenAI models. You will help drive technical direction, collaborate across functions, and shape the future of Meta’s AI data platforms.
Job Responsibility:
Serve as a technical leader for the design and architecture of scalable dataloading and storage solutions for GenAI workloads within WS Airstore
Drive architectural decisions and set technical direction for high-throughput, low-latency data pipelines supporting large language and multimodal models
Develop and optimize data storage formats, access patterns, and caching strategies for GenAI use cases
Collaborate with research, infrastructure, and product teams to understand evolving GenAI data requirements and translate them into robust engineering solutions
Define use cases, develop methodologies, and establish benchmarks to evaluate and improve data infrastructure approaches
Mentor and provide technical guidance to engineers across the organization, fostering best practices in large-scale data engineering and distributed systems
Stay abreast of industry and Meta-wide trends in AI data infrastructure, storage technologies, and distributed computing
Requirements:
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Demonstrated experience as a technical leader in the design and architecture of large-scale data infrastructure or storage systems
Experience driving cross-functional engineering efforts and influence architectural direction without direct management responsibilities
Track record of planning and executing technical roadmaps, with shorter-term projects laddering to long-term vision
Experience with distributed systems and large-scale data management
Experience mentoring and influencing engineers across organizations
Specialized experience in one or more of the following domains. Data infrastructure for AI/ML (GenAI, LLMs, multimodal models), Distributed storage systems, data lakes, or cloud object stores, High-performance data pipelines and ETL frameworks, or Planet scale data base / data store
Proficiency in C/C++, Python, or similar languages for developing data infrastructure
Experience developing and optimizing data systems for AI/ML workloads
Nice to have:
Familiarity with modern data storage formats (e.g., Parquet, ORC) and data lake architectures
Experience with Meta’s internal data infrastructure (Airstore, Scuba, Presto, etc.) is a plus