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Staff/Sr. ML Compute Efficiency Engineer

Santa Clara, CA, US • Posted 2 days ago • Updated 8 hours ago

Full Time

On-site

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Job Details

Skills

High Performance Computing
IDLE
Recovery
Scheduling
Orchestration
Artificial Intelligence
Software Engineering
PyTorch
JAX
C
C++
Python
Machine Learning (ML)
Research
Training
Computer Science
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Summary

Scaling machine learning workloads across thousands of GPUs and TPUs creates challenges that few engineers ever encounter. In Apple's Machine Learning Platform Technologies organization, we build the infrastructure that powers large-scale ML training and inference workloads, bringing together expertise in distributed systems, machine learning infrastructure, and high-performance computing.

As a performance engineer in the ML Compute Efficiency team, you'll tackle ambiguous systems challenges, identify inefficiencies and build solutions that maximize accelerator utilization, reduce idle and fragmented capacity, and minimize recovery periods. This includes analyzing accelerator performance, digging into various parallelism techniques, and refining workload scheduling and orchestration across the compute fleet.

Experience with large-scale distributed systems for AI/ML workloads running on GPUs or TPUs.\nStrong software engineering skills with experience developing and optimizing training frameworks (e.g. PyTorch, JAX) using C/C++ or Python. \nExperience working on cross-functional projects with ML research and infrastructure teams.\nFamiliarity with model architectures and various training techniques. \nBachelor's degree in Computer Science or equivalent experience, with 7+ years of industry experience.

Have a track record of delivering transformative performance improvements on large scale infrastructure. \nAbility to analyze ambiguous, distributed systems problems and articulate both high-level strategic metrics and underlying technical complexity.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 90733111
Position Id: fde904f7dadea28f1f7f59d1b09e8af0
Posted 2 days ago

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Staff/Sr. ML Compute Efficiency Engineer

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