Backed by SOSV.
About the role
Applicants should have expertise in Python (including NumPy, pandas, and other packages) and either PyTorch or TensorFlow. Deep understanding of machine learning fundamentals (gradient descent, cross-validation, ROC curves, confusion matrices) are necessary. Knowledge of classical machine learning (e.g., support-vector-machines, logistic regression) are valued.
What they're looking for
- Software expertise: Python and associated numerical and analytics packages (NUMPY, PANDAS, etc.), git, PYTORCH or TensorFlow, CI/CD pipelines and regression testing
- AI/ML expertise: Machine learning fundamentals, Deep knowledge of state-of-the-art in any of the following: computer vision (preferred), natural language processing, classical machine learning, Bayesian models, etc
- B.S., preferably M.S. or Ph.D in engineering, math, computer science, or related field
- Excellent technical communication skills
- Ability to work in Durham, NC (relocation assistance available)
- Eligibility for US security clearance (US citizenship is required)
More about this role
CoVar is a small AI/ML R&D software company in Durham, NC, that uses artificial intelligence to solve problems that matter. We develop AI/ML tools to help the DoD detect enemies and threats, help biomedical researchers find new cures for diseases, and help monitor machinery to prevent injuries and environmental catastrophes. We are passionate engineers, dedicated to pushing the bounds of what AI/ML can do in the real world.
You will help CoVar develop software and machine learning algorithms to solve real-world customer problems. You will work with data, develop algorithms, evaluate results, and write the production code that goes onto real-world systems. You will have the opportunity to present your work to high-level customers in the DoD and in the industry. The position includes opportunities to publish novel work in both classified and unclassified settings.
Applicants should have expertise in Python (including NumPy, pandas, and other packages) and either PyTorch or TensorFlow. Deep understanding of machine learning fundamentals (gradient descent, cross-validation, ROC curves, confusion matrices) are necessary. Knowledge of classical machine learning (e.g.,...
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