GPU Software Engineer developing GPU-accelerated software for real-time analysis on DNA sequencing instruments. Collaborating on neural network implementations and complex technical solutions.
Responsibilities
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
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