Senior Software Engineer - Ml Infrastructure

Applied Intuition

Sunnyvale, California, United States
Base: $153,000 - $222,000 usd annually; equity: op...
On-site
Distributed cloud gpu training experience
End-to-end machine learning pipeline development
Production full-stack machine learning challenges
Applied Intuition is seeking a Senior Software Engineer specialized in machine learning infrastructure to work on the entire ML lifecycle, from dataset generation to deployment. The company values a culture of ownership, collaboration, and excellence, offering a competitive salary range along with various benefits

Job Summary

  • Applied Intuition is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet.
  • This role involves designing and implementing distributed cloud GPU training approaches for deep learning model training and evaluation.
  • Compensation includes base salary ranging from $153,000 to $222,000 USD annually, plus equity and comprehensive benefits.

Matching Summary

Match Score: 85

Applied Intuition is seeking a Senior Software Engineer specialized in machine learning infrastructure to work on the entire ML lifecycle, from dataset generation to deployment. The company values a culture of ownership, collaboration, and excellence, offering a competitive salary range along with various benefits.

Salary

Base: $153,000 - $222,000 USD annually; Equity: Options and/or restricted stock units included; Benefits: Comprehensive health, dental, vision, life, disability, 401k match, and stipends

Skills & Requirements

Must-have

  • Distributed cloud GPU training experience
  • End-to-end machine learning pipeline development
  • Production full-stack machine learning challenges
  • Bachelor's degree in Computer Science or equivalent

Nice-to-have

  • Experience with Airflow and Flyte orchestration systems
  • Knowledge of PyTorch and TensorFlow modeling frameworks
  • Experience with model serving platforms like Triton
  • Judgment on open source versus in-house build decisions

Key Requirements

  • 3+ years of professional experience
  • Bachelor's degree in Computer Science or Software Engineering
  • Experience addressing production full-stack machine learning challenges

Work Rights

Not specified

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