Staff Machine Learning Engineer, ML Performance & Optimization
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The role
Take ownership as a Staff ML Engineer focused on ML performance and optimization across multi-platform deployments.
You will optimize neural architectures and systems for high performance on GPU/TPU hardware, including onboard and simulation platforms.
Develop post-training techniques like quantization and kernel-level optimizations to reduce latency and memory footprint for real-time constraints.
Experiment with new architectures (sparse models) and decoding strategies (speculative decoding) to boost inference speed.
Enhance training efficiency for large models and fine-tuning in data-heavy pipelines, while collaborating with ML infra, hardware, and research teams.
This hybrid role emphasizes hands-on optimization and cross-team collaboration to scale production-grade models.
You will optimize neural architectures and systems for high performance on GPU/TPU hardware, including onboard and simulation platforms.
Develop post-training techniques like quantization and kernel-level optimizations to reduce latency and memory footprint for real-time constraints.
Experiment with new architectures (sparse models) and decoding strategies (speculative decoding) to boost inference speed.
Enhance training efficiency for large models and fine-tuning in data-heavy pipelines, while collaborating with ML infra, hardware, and research teams.
This hybrid role emphasizes hands-on optimization and cross-team collaboration to scale production-grade models.
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