Intern Machine Learning Engineer

ARM-posted 4 months ago

Intern

Hybrid • San Jose, CA

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The mission of Central Technology - Machine Learning (CT-ML) is to create technology to enable all ML Compute on ARM. We believe that advancing innovative machine learning (ML) technology requires a collaborative approach across hardware, software, and algorithms. The CT-ML Algorithms team acts as the Center of Excellence for ML content analysis, algorithm development, and tools, driving this unified effort forward.

Develop in depth understanding of ML workload, and develop algorithms and optimization techniques to drive PPA (Performance, Power, Area) on current and future Arm platforms.
Conduct ML workload analysis and serve as the ML Workload experts to support all phases of ARM IP development.
Devise algorithm and prototyping on current platforms and to support future architecture exploration.
Develop internal tooling capabilities to support algorithmic and architecture exploration.

Currently enrolled and studying towards an Electrical or Computer Engineering Degree (Masters or PhD students welcome). Candidates with alternative degrees will also be considered if they have relevant experience.
Deep knowledge in machine learning, deep learning, and neural network design, optimization, and compression techniques.
Overall high-level knowledge of computer architecture, systems, and HW-SW co-design.
Ability to develop and work with large software systems in programming languages like Python.
Knowledge of innovative deep learning libraries such as Tensorflow, and Pytorch.
Willing to learn and train large deep learning models on GPU-based systems.

Experience with ML model design, optimization, and HW-SW co-development methodology.
ML Model Optimization techniques targeting PPA (Performance, Power, and Area) of neural networks on ARM compute platforms.
Adaptability to the fast paced ML industry and willingness to learn new technology in a multifaceted environment.

Regular feedback and development opportunities.
Social activities to connect with peers.
End of internship celebration.
Opportunity to be considered for future Graduate positions (subject to performance).
Competitive salary and rewards package.
On-the-job learning and mentoring/buddy schemes.

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