Required Education, Experience, and Qualifications
- Demonstrated passion for artificial intelligence (AI) and machine learning (ML)
- Proficient in Python and Linux
- Proficient in at least one deep learning framework such as PyTorch, TensorFlow, Caffe2, or others
- Excellent understanding of AI techniques and algorithms, such as deep learning, CNNs, RNNs, decision trees, clustering, ensembles, etc.
- Bachelor’s degree or higher in AI/ML, computer science, statistics, applied mathematics, physics, bioinformatics, electrical engineering, or a similarly quantitative discipline
- Solid foundation in mathematics, especially probability, classical and Bayesian statistics, linear algebra, differential equations, and calculus
Desirable Education, Experience, and Qualifications
- 2+ years industry experience with software development in a production environment, especially with AI/ML
- A portfolio of relevant work available for review online, to include course assignments
- Proficiency in C++, C#, and other programing languages
- Experience developing algorithms in a cloud computing environment
- Experience with AWS and/or Azure
- Knowledge of current deep learning models and commercially-available deep learning products for life sciences, genomics, materials science, computer vision, etc.
- Experience with big data, data science, data wrangling, and databases
- Understanding and/or experience working in an Agile/Scrum development environment
Knowledge, Skills, Abilities
- Excellent problem-solving abilities in both familiar and unfamiliar domains
- Able to comprehend complex data and use it to troubleshoot issues
- Ability to understand foundational research in ML and an aptitude for scientific R&D across numerous disciplines
- Desire to continuously improve processes and practices
- Discipline, pride, and professionalism to write clean code
- Excellent written and verbal communication skills
- Ability to engage with innovation leaders globally and high-level R&D leaders within a large, global company
- Enthusiastic about new technologies and how AI/ML can accelerate R&D, yet realistic in its potential and its application
