Software Engineer I - AI/ML, AWS Neuron Distributed Training

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

$127K-$185K not verified - source not recorded; timestamp not recorded

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

39% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

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About this role

Software Engineer I - AI/ML, AWS Neuron Distributed Training Cupertino, California, USA Annapurna Labs designs silicon and software that accelerates innovation. Our custom chips, accelerators, and software stacks enable us to tackle unprecedented technical challenges and deliver solutions that help customers change the world. AWS Neuron is the complete software stack powering AWS Trainium (Trn2/Trn3), our cloud scale Machine Learning accelerators and we are seeking a Senior Software Engineer to join our ML Distributed Training team. In this role, you will be responsible for the development, enablement, and performance optimization of large scale ML model training across diverse model families. This includes massive scale pre-training and post-training of LLMs with Dense and Mixture-of-Experts architectures, Multimodal models that are transformer and diffusion based, and Reinforcement Learning workloads. You will work at the intersection of ML research and high performance systems, collaborating closely with chip architects, compiler engineers, runtime engineers and AWS solution architects to deliver cost-effective, performant machine learning solutions on AWS Trainium based systems. Key job responsibilities You will contribute to the design and implementation of distributed training solutions for large-scale ML models running on Trainium instances. A significant part of your work will involve extending and optimizing popular distributed training frameworks including FSDP, torchtitan, and Hugging Face libraries for the Neuron ecosystem. A core focus of this role involves developing and optimizing mixed-precision and low-precision training techniques. You will work with BF16, FP8, and emerging numerical formats to improve training throughput while maintaining model accuracy and convergence quality. This

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