About Sway AI
Sway AI is a venture-backed start-up in the Boston area. It was founded by proven entrepreneurs who have built several successful companies. Enterprise AI is a trillion-dollar business, however, 85% of AI projects fail. There are still high barriers to entry that make it hard for enterprises to use AI without large data science teams and multi-million-dollar budgets. Sway AI believes that “there’s a better way to do AI” by helping enterprises build without expensive upfront investments in AI tools or skillsets.
Sway AI is seeking motivated principal/senior data scientist who is passionate about innovation, quality and technology; people with high attention to detail and edge-case analytical thinking. Join a experienced and talented group of entrepreneurs, scientists, and engineers with a groundbreaking idea to re-architect how AI/ML applications are built and deployed.
Who Are You?
- PhD or MS, or equivalent, in mathematics, CS with the focus on machine learning
- Experience and expertise in one of the ML domains, e.g., computer vision, natural language processing, statistical learning theory
- 5+ years of professional industry experience as a Data Scientist/Machine Learning Engineer focused on developing, deploying and monitoring machine learning systems at scale
- Significant peer-reviewed scientific contributions in premier journals and conferences
- Experience writing code in Python with documentation for reproducibility
- Experience with one or more of the DNN frameworks like TensorFlow or PyTorch
- Familiarity with cloud computing services such as AWS
- Passionate and collaborative team player with drive to work in a fast-paced environment
- Experience with tools such as AWS SageMaker, Airflow, MetaFlow/KubeFlow is a plus
- Experience in SQL or Non-SQL databases
- Experience with gradient descent and other forms of first and second order derivative-based optimization
- Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts
- Experience with defining research and development practices in an applied environment