Amazon.com Services LLC
Technology
SoftwareEngineerII-AI/ML,AWSNeuron
Neural analysis suggests this role is
optimal for Mid candidates.
“Software Engineer II- AI/ML, AWS Neuron at Amazon.com Services LLC. Skills: AI/ML, AWS Neuron, Deep Learning, GenAI. Lead distributed training support. Enable frameworks”
Industry & Context.
Root cause analysis; Debugging; Troubleshooting
What They're Looking For.
Must Have
3+ years professional software development, Bachelor's degree or above in engineering, 3+ years design or architecture experience, Machine Learning and Large Language Model fundamentals, Knowledge of system performance, Knowledge of memory management, Knowledge of parallel computing principles, Experience in debugging large-scale systems, Experience in profiling large-scale systems, Experience implementing software engineering best practices
Nice to Have
Experience with PyTorch, Experience with JIT compilation, Experience with AOT tracing, Experience programming with at least one software programming language, Experience with CUDA kernels, Experience with ML/low-level kernels, Experience in computer architecture, Experience with distributed training at scale
What You'll Do.
Lead distributed training support
Design machine learning models
Develop machine learning models
Optimize machine learning models
Design distributed computing architecture
Implement distributed computing architecture
Profile system performance
Optimize system performance
Conduct performance analysis
Conduct comprehensive testing
Enable ML models on AWS accelerators
Optimize ML models on AWS accelerators
Develop optimization techniques
Debug performance issues
Optimize memory usage
Shape inference stack
Design software solutions
Code software solutions
Resolve software defects
Build high-impact solutions
Participate in design discussions
Participate in code review
Communicate with stakeholders
Drive business decisions
How You'll Work.
Team & Collaboration
Cross-functional team; Compiler teams; Runtime teams; Framework teams; Hardware teams; Applied scientists; System engineers; Product managers; Open Source Community
Communication Scope
Communicate with stakeholders
Full Job Description
The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon’s custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Training Enablement and Foundation team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work at the intersection of machine learning, high-performance computing, and distributed architectures, where you'll help shape the future of AI acceleration technology You will architect and implement business critical features, and mentor a brilliant team of experienced engineers. We operate in spaces that are very large, yet our teams remain small
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