AI Software Engineer

Solidigm • Portland, OR

2h • $116,240 - $181,700

About The Position

Come join a very collaborative and innovative team to write the next chapter in AI. We value hands-on engaged individuals and provide an environment of growth and support. Conduct design and development to build and optimize deep learning software. Design, develop & optimize for deep learning training and inference frameworks. Implement various distributed algorithms such as model/data parallel frameworks, parameter servers, dataflow based asynchronous data communication in deep learning frameworks. Transform computational graph representation of neural network model. Develop deep learning primitives in math libraries. Profile distributed DL models to identify performance bottlenecks and propose solutions across individual component teams. Optimizing code for various computing hardware backends. Interacting with deep learning researchers and experience with deep learning frameworks. Responsible for preparing data for ML models at scale, building appropriate inference interfaces for ML model consumption, enabling ML Ops for continuous delivery and automation of ML pipelines, and/or building and sustaining AI productization platforms.

Requirements

• MS degree with 6 years of experience, or a PhD with up to 2 years experience
• 6-8 years of experience in AI, and Data Science
• Hands-on model development and up to date with AI technologies
• Experience developing in Python PyTorch and/or Tensorflow
• Focus on computer vision use cases and models

Nice To Haves

• Direct work and interactions with customers
• Robotics and physical AI

Responsibilities

• Conduct design and development to build and optimize deep learning software.
• Design, develop & optimize for deep learning training and inference frameworks.
• Implement various distributed algorithms such as model/data parallel frameworks, parameter servers, dataflow based asynchronous data communication in deep learning frameworks.
• Transform computational graph representation of neural network model.
• Develop deep learning primitives in math libraries.
• Profile distributed DL models to identify performance bottlenecks and propose solutions across individual component teams.
• Optimizing code for various computing hardware backends.
• Interacting with deep learning researchers and experience with deep learning frameworks.
• Responsible for preparing data for ML models at scale, building appropriate inference interfaces for ML model consumption, enabling ML Ops for continuous delivery and automation of ML pipelines, and/or building and sustaining AI productization platforms.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level