We are building a unified algorithmic architecture to achieve human-level intelligence in vision, language, and motor control. Currently, we are focused on visual perception problems, like recognition, segmentation, and scene parsing. We are interested in general solutions that work well across multiple sensory domains and tasks.
Using inductive biases drawn from neuroscience, our system requires orders of magnitude less training data than traditional machine learning techniques. Our underlying framework combines advantages of deep architectures and generative probabilistic models. We use modern software engineering practices, and we strive to maintain a codebase and a culture that are a joy to work in.
We have raised ~$70M in funding and are not constrained by publication, grant applications, or product development cycles. At Vicarious, there is room to develop new approaches that would otherwise not be supported in academia or industry.
You will join a small, tightly knit collective of extraordinary engineer scientists. Everyone works on our full stack, from algorithms to low level optimizations to GUI code and back.
As part of our team, you will...
- put your algorithm and math skills to work in solving the hardest problems in learning and inference in hierarchical models.
- make decisions about how to translate complex ideas to working solutions while keeping a keen eye for computation/accuracy/memory tradeoffs.
- design controlled experiments to show particular performance aspects of the systems and large scale experiments to show statistical robustness.
- write infrastructure software to scale our systems and data visualization routines to understand what is happening inside.
- document your inventions for patents and publications.
- keep yourself updated with advances in the field of machine learning and neuroscience.
The craftsmanship of building elegant algorithms and tight implementations are part of our company DNA. We work hard to maintain a codebase and a culture that are a joy to work in.
Desired skills and experience
- Preferred PhD or Masters in CS/EE or a related discipline or Masters in CS/EE with relevant research experience. We also consider exceptional applicants with other backgrounds.
- Experience building hierarchical vision systems and publishing relevant papers in CVPR/NIPS/ICML is a big plus.
- Extensive programming skills, ideally in Python and C, and a track record of translating ideas into prototypes quickly.
- Solid fundamentals in linear algebra, probability theory, signal processing, and optimization.
- Experience developing and testing ideas in a large scale setting.
- Experience with belief propagation and approximation methods.
- Knowledge of biologically inspired models of vision.
- Interest in neuroscience a plus.
- Experience working in an interpreted environment like MATLAB or Mathematica also a plus.