Senior Principal Machine Learning Engineer, Foundational Models
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The role
Lead the design, development, and delivery of large-scale ML systems spanning foundation models, reinforcement learning, data pipelines, and production platforms.
Own the end-to-end lifecycle from model architecture selection and data strategy to distributed training and deployment in cloud or HPC environments.
Drive foundation model engineering, translating experimental ideas into scalable, high-performance code with collaboration from research scientists.
Architect distributed training infrastructure using FSDP, Megatron-LM, and DeepSpeed, optimizing data processing and model parallelism for throughput.
Mentor senior engineers, set engineering standards, and partner with product, data engineering, and platform teams to deliver multimodal AEC data solutions.
Establish robust MLOps practices, including model evaluation, versioning, monitoring, and reproducibility to ensure reliable production systems.
Own the end-to-end lifecycle from model architecture selection and data strategy to distributed training and deployment in cloud or HPC environments.
Drive foundation model engineering, translating experimental ideas into scalable, high-performance code with collaboration from research scientists.
Architect distributed training infrastructure using FSDP, Megatron-LM, and DeepSpeed, optimizing data processing and model parallelism for throughput.
Mentor senior engineers, set engineering standards, and partner with product, data engineering, and platform teams to deliver multimodal AEC data solutions.
Establish robust MLOps practices, including model evaluation, versioning, monitoring, and reproducibility to ensure reliable production systems.
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