Director of Machine Learning

We are looking for an experienced and accomplished ML/AI scientist who is strong in both practical R&D and fundamentals having deep and broad expertise in several or at least a few applied science disciplines. Our current focus areas are time-series analysis, temporal modeling, multi-modal spatiotemporal modeling, and related areas. This person will have an excellent overall understanding of state-of-the-art machine learning algorithms and technology, exceptional publication records (e.g., NeurIPS, ICML, ICLR, AAAI, KDD, etc.), and good knowledge and experience in computer science and engineering (e.g., how we should use CPUs/GPUs for efficient training and inference for diverse use cases).They should also have extensive experience and skills in collaboration with software engineering teams to enable the scaling and productization of their algorithms. The ideal candidate will have 10+ years of industry experience with 5+ years of experience managing team(s) of accomplished research/data scientists and engineers.

Responsibilities

• Develop cutting-edge ML algorithms esp. focusing on the time-series domain but also in areas such as online classification, regression, supervised/unsupervised learning, reinforcement learning, anomaly detection, pattern recognition, and hybrid ML algorithms.
• Lead projects with complete independence and help program/product managers even when business, product, and technical strategies are evolving and the problems to be solved are not well understood.
• Work with program/project managers to define use cases, collect data, and benchmark the results.
• Collaborate with other applied scientists to experiment, and develop algorithms/ prototypes that advance the state-of-the-art in industrial AI.
• Work with software engineers to provide scaling and production support for ML/AI algorithms
• 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

• Ph.D. degree in Machine Learning, Computer Science, Statistics, Electrical Engineering, or related fields.
• At least 10 years of experience doing exceptional ML/AI research as demonstrated by both scientific publications in top venues and potential industrial impact
• Has deep and broad expertise across several applied science disciplines, is scientifically versatile, and demonstrates scientific and industrial maturity.
• Practical 5+ years experience in applying ML/AI to complex business problems.
• 5 years of experience managing teams of research scientists/engineers and data scientists (experience managing geographically distributed, multi-cultural teams a plus)
• Exceptional project management skills
• Hands-on experience programming in Python, Matlab, R, Java, C++, or other programming languages.
• Experience in large-scale ML systems and enabling technologies including commercial cloud stacks, resource provisioning/orchestration, and scaling methodologies (e.g., distributed optimization)