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Internship

Artificial Intelligence Intern (Summer 2024)

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GH Labs

•

19d ago

🚀 Summer Internship

Seattle

⌛ Closed
Applications are closed

Summer Internship

Data•Seattle

Description

  • We are seeking a motivated and innovative Artificial Intelligence (AI) Intern to join our AI team and contribute to the advancement of our cutting-edge deep learning systems. This internship provides a unique opportunity to work with other Machine Learning Engineers, subject scientists, and clinical experts in addressing the key challenges deep learning systems encounter when implemented and deployed in low resource clinical settings. Developing machine learning systems for medical datasets involves confronting unique challenges and complexities, including issues related to privacy, data heterogeneity, class imbalance, subjective ground truths, small datasets, and limitations in resource-constrained computing. Intern will gain exposure to these challenges and contribute to the development of machine learning algorithms aimed at addressing these specific issues.

Requirements

  • Enrolled in Master/Doctorate program in Mathematics, Computer Science, Electrical Science, Data Engineering, or other relevant engineering discipline.
  • Strong understanding of deep learning fundamentals and frameworks (PyTorch, TensorFlow, etc).
  • Proficiency in programming languages such as Python and experience with relevant libraries.
  • Experience in submitting training jobs on Linux-based high-performance computer devices/servers.
  • Experience in monitoring, debugging, and optimizing deep learning experiments.
  • Familiarity with efficient deep learning optimization techniques and benchmarking tools.
  • Familiarity with SQL database and extracting data points form structured and unstructured datasets.
  • Prior experiment in working with medical datasets preferred.
  • Knowledge of algorithms for dealing with noisy ground truths preferred.
  • Knowledge of optimizations methods for low-latency algorithms preferred.
  • Familiarity with customization of loss functions for various techniques such as supervised, unsupervised, and adversarial preferred.
  • Required Competencies:
  • GH Labs Success Factors: We believe there are a few prioritized key capabilities and behaviors that are critical to success for all roles at GH Labs. They are:
  • Demonstrates Collaboration: actively listens and works with others openly and transparently to create an environment where diverse viewpoints are valued, and information is shared for the purposes of solving problems and achieving collective goals. Seeks collaborative solutions as the best path to sustainable change.
  • Accelerates Impact: takes risks, proves/disproves concepts quickly and with a growth mindset, solves creatively, and partners strategically to achieve results and deliver impact on GH Labs mission.
  • Promotes Accountability: holds self and others accountable so that success is celebrated, and failure is understood and addressed.
  • Establishes Trust: interacts, shares, and receives information and feedback in a way that builds trust and gains the confidence of others.

Education requirements

Currently Studying
Undergraduate

Area of Responsibilities

Data

Responsibilities

  • Algorithm Training Framework Development: Contribute to the development of a deep learning algorithm training framework specifically designed to handle noisy ground truths associated with healthcare data. Implement strategies to mitigate ground truth noise impact.
  • Exploration of Deep Learning Methods: Experiment with supervised, unsupervised, and adversarial learning methods to assess their applicability to address variations in ground truths. Explore new loss functions, uncertainty estimation of data points, and further implement learnings from active learning approaches.
  • Optimize Deep Learning Models: Analyze and optimize existing deep learning models trained on healthcare data. Implement techniques to improve model efficiency, speed, and memory usage.
  • Execute extensive experiments on high-performance computing devices, aggregate findings from various experiments, and deliver routine progress updates to the team.
  • Benchmarking and Performance Analysis: Utilize benchmarking tools and other performance predictors to characterize the inference speed and memory requirements of optimized models. Provide detailed reports and insights.

Details

Work type

Full time

Work mode

office

Location

Seattle