Hybrid GPU Software Engineer

Posted last month

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About the role

  • Develop GPU-accelerated software targeted for real-time analysis on a DNA sequencing instrument
  • Implement neural network algorithms on GPU, optimized for our unique characteristics of very high throughput and model types
  • Be proficient in C++ and CUDA, and have familiarity in Python for modeling-related tasks
  • Self-motivation to individually research and design solutions for complex technical problems
  • Author dependable, readable, maintainable, and well-structured code, and encourages best practices with the team

Requirements

  • BS in Computer Science, Computer Engineering, or related field plus 5 years of direct related experience
  • MS/PhD is a plus
  • Object-oriented programming in C++ and experience with GPU-accelerated C/C++ libraries such as CUDA, cuDNN, or Thrust
  • Advanced experience in systems programming, specifically writing, debugging, and optimizing parallel (CPU and GPU) Linux applications
  • Good understanding of machine learning, particularly neural networks
  • Experience with a deep learning framework, such as PyTorch, JAX, or TensorFlow
  • Basic experience with Python
  • Ability to multitask and work with little direction in a collaborative fast-paced environment
  • Strong problem-solving skills and ability to track software issues to successful resolution
  • Excellent oral and written communication skills, and interpersonal communication skills with internal and external partners

Benefits

  • A competitive salary with a rich benefits package
  • Discretionary annual bonus based on performance

Job title

GPU Software Engineer

Job type

Experience level

Mid levelSenior

Salary

$94,500 - $227,200 per year

Degree requirement

Bachelor's Degree

Location requirements

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