Reality Labs Research (RL-R) brings together a diverse and highly interdisciplinary team of researchers and engineers to create the future of augmented and virtual reality. As a member of the Codec Avatars ML Compute Infrastructure team, you'll have the exciting opportunity to contribute to the advancement of our Codec Avatar technology. Your role will involve delivering data, tools, and libraries within our super clusters, playing a crucial part in our technological progress. Our team cultivates an honest and considerate environment where self-motivated individuals thrive. We encourage a strong sense of ownership and embrace the ambiguity that comes with working on the frontiers of research. In this research engineer role on the Codec Avatar ML Compute team, you will serve as the point of contact for Meta's research GPU super clusters, parallelizing massive ML models and data, and optimizing other compute resources to enable groundbreaking research in relightable avatars, full-body avatars, and generative AI for codec avatars.
Research Engineer - Codec Avatar ML Compute Team Responsibilities

• Build efficient and scalable machine learning tooling for the GPU clusters within Meta research labs, a heterogeneous environment containing diverse system architectures and research workload
• Build efficient and scalable data tooling for massive ML training data preprocessing and postprocessing using thousands of CPU / GPUs nodes
• Provide on-call support and lead incident root cause analysis through multiple infrastructure layers (compute, storage, network) for GPU clusters and act as a final escalation point
• Work side by side with research scientists and engineers to take full advantage of modern GPUs for large scale multi-GPU training jobs impact
• Collaborate in a diverse team environment across multiple scientific and engineering disciplines, making the architectural tradeoffs required to rapidly train large scale ML models
• Provide guidance to other engineers on best practices to build mature tools which are highly reliable, secure, and scalable
• Influence outcomes within your immediate team, peer engineering teams, and with cross-functional stakeholders
• Ability to work independently, handle large projects simultaneously, and prioritize team roadmap and deliverables by balancing required effort with resulting

Minimum 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.
• 3 years experience coding in C and Python
• Experience in large scale ML system performance measurement, logging, and optimization
• Experience in writing system level infrastructure, libraries, and applications
• Prior experience in ML libraries such as PyTorch, TensorFlow, or cuDNN
• Experience with software development practices such as source control, unit testing, debugging and profiling
• Experience in developing performant software and systems

Preferred Qualifications

• Prior experience in large scale machine learning model training, including model parallelization strategies
• Prior experience in machine learning model compiler
• Prior experience in cluster coordination and strategy planning, including collecting/understanding needs of researchers, developing tools to improve research experience, providing guidance on best practices, coordinating distribution of compute/storage resources, forecasting compute/storage needs, and developing long-term user experience/compute/storage strategies
• Prior experience building tooling for monitoring and telemetry for large scale supercomputers
• Prior experience in debugging performance issues for large scale ML training tasks
• Prior experience in GPGPU development with CUDA, OpenCL or DirectCompute
• Familiar with Linux observability tools, such as eBPF

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