AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - Pre-training Infrastructure

As an engineer on ML Compute team, your work will include: * Drive large-scale pre-training initiatives to support cutting-edge foundation models, focusing on resiliency, efficiency, scalability, and resource optimization.

• Enhance distributed training techniques for foundation models.
• Research and implement new patterns and technologies to improve system performance, maintainability, and design.
• Optimize execution and performance of workloads built with JAX, PyTorch, XLA and CUDA on large distributed systems.
• Leverage high-performance networking technologies such as NCCL for GPU collectives and TPU interconnect (ICI/Fabric) for large-scale distributed training.
• Architect a robust MLOps platform to streamline and automate pretraining operations.
• Operationalize large-scale ML workloads on Kubernetes, ensuring distributed trainings are robust, efficient, and fault-tolerant.
• Lead complex technical projects, defining requirements and tracking progress with team members.
• Collaborate with cross-functional engineers to solve large-scale ML training challenges.
• Mentor engineers in areas of your expertise, fostering skill growth and knowledge sharing.
• Cultivate a team centered on collaboration, technical excellence, and innovation.

• Bachelors in Computer Science, engineering, or a related field
• 6+ years of hands-on experience in building scalable backend systems for training and evaluation of machine learning models
• Proficient in relevant programming languages, like Python or Go
• Strong expertise in distributed systems, reliability and scalability, containerization, and cloud platforms
• Proficient in cloud computing infrastructure and tools: Kubernetes, Ray, PySpark
• Ability to clearly and concisely communicate technical and architectural problems, while working with partners to iteratively find, * Advance degrees in Computer Science, engineering, or a related field
• Proficient in working with and debugging accelerators, like: GPU, TPU, AWS Trainium
• Proficient in ML training and deployment frameworks, like: JAX, Tensorflow, PyTorch, TensorRT, vLLM

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