- Develop, optimize, and maintain automated data ingestion pipelines to move massive datasets at petabytes scale into GPU research supercluster
- Provide on-call support and lead incident root cause analysis through multiple data engineering layers (compute, storage, network) for GPU clusters and act as a final escalation point
- Collaborate in a diverse team environment across multiple scientific and engineering disciplines, making the architectural tradeoffs required to rapidly deliver software and infrastructure solutions
- Leverage the scale and complexity of the larger Meta production infrastructure to accelerate our Codec Interaction and Avatars projects
- Influence outcomes within your immediate team, peer engineering teams, and with cross-functional stakeholders
- Works independently, handles large projects simultaneously, and prioritizes team roadmap and deliverables by balancing required effort with resulting impact
- 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.
- 3+ years experience coding in at least one of the following languages: C++, Python, or Rust
- Experience in building large scale data intensive applications
- Experience in building and automating web services
- Experience in writing system level infrastructure, libraries, and applications
- Experience with software development practices such as source control, code reviews, unit testing, debugging and profiling
- Proven track record of shipping data processing pipelines for computer vision or compute graphics or machine learning applications
- Experience in crafting and maintaining large scale machine learning datasets
- Experience in developing performant software and systems
- Thorough understanding of Linux operating system, including the networking subsystem
- Experience in distributed system performance measurement, logging, and optimization
- Experience with Python library management systems such as Conda
- Experience with managing HPC scheduler libraries like Slurm, Kubernetes
- Prior experience in cluster oncall operations, including troubleshooting server/scheduler/storage errors, maintaining compute/storage environments/libraries/tools, helping onboard users to the cluster, and answering general questions from users
- Prior experience in cluster coordination and strategy planning, including collecting/understanding needs of users, developing tools to improve user experience, providing guidance on best practices, forecasting compute/storage needs, and developing long-term user experience/compute/storage strategies
- Prior experience building tooling for monitoring and telemetry
- Prior experience building PaaS or internal clouds
- Prior experience in developing/managing distributed network file systems
- Prior experience in network security
- Experience in database and data management systems at scale
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