We are looking for experienced and accomplished Applied Scientists for Machine Learning (ML) and Data Science (DS). You are supposed to be strong in both practical R&D and fundamentals with deep and broad expertise in several or at least a few applied science disciplines. You should have a good understanding of the state-of-the-art ML algorithms and methods, exceptional publication records (e.g., NeurIPS, ICML, ICLR, KDD, CVPR, ICCV, etc.), and good knowledge and experience in computer science and engineering (e.g., how to use CPUs/GPUs for efficient training and inference for diverse use cases). You should also have experience and skills in collaboration with software engineering teams to enable the scaling and productization of ML algorithms.
Responsibilities
- Develop cutting-edge Machine Learning algorithms in time-series domain and technical areas such as online classification, regression, supervised/unsupervised learning, anomaly detection, pattern recognition, or hybrid ML algorithms.
- Collaborate with other applied scientists to experiment and develop algorithms/prototypes that advance state-of-the-art industrial AI.
- Work with software engineers to provide support for scaling and productization of algorithms.
- Work with PMs to define use cases, collect data, and benchmark the results.
- Lead projects independently and help PMs in a dynamic environment where business, product, and technical strategies are evolving even when problems are not well understood yet.
- Contribute to Gauss Labs's intellectual property pools through patents and technical publications.
- Contribute to Gauss Labs's research advancement by publishing technical papers at external conferences and journals.
Key Qualifications
- MS or Ph.D. in Machine Learning, Data Science, Statistics, Computer Science, Electrical Engineering, or related fields.
- 3+ years of experience doing exceptional Machine Learning research as demonstrated by both scientific publications in top venues and solutions to resolve complex business problems for potential industrial impact.
- Hands-on experience programming in Python or other modern programming languages.
- Experience in large-scale ML systems and related technologies, including commercial cloud stacks, resource provisioning/orchestration, and scaling methodologies (e.g., distributed optimization).