Vicarious aims to transform robotics by creating robots with human level performance on real-world manipulation tasks. We are passionate about changing the world with science and software, and we are looking for exceptional people to join us in that mission.
As the Technical Product Manager of Cloud ML, you will play a critical role in defining the different cloud pipelines that enable our intelligent vision systems, using neural network, probabilistic graphical model, and traditional computer vision approaches.
Generate and maintain Product Requirements Docs for various cloud pipelines
Clarify scope and provide guidance from end user perspective to those executing the work
Become an expert in methods of segmentation, 3D reconstruction, data augmentation, pose estimation, simulation
Identify edge cases unsolved by our current approaches
Assess technical proposals for validity and fit with market needs
Manage risk profile of research for each desired feature / enhancement
Ensure that systems developed are built to scale
Coordinate development of features with different vision and AI teams
Triage inbound support tickets relating to cloud pipelines
Reinforce simplicity and generality with development team
Take responsibility for the overall success of the product
3+ years experience as a Product Manager at a software company
Bachelors or Masters in engineering discipline
Experience with machine learning systems either in industry or academia
Experience with computer vision in some capacity
Nice to have
Programming skills in Python
Familiarity with Google Cloud Platform
Familiarity with Tensorflow
Vicarious is proud to be an equal opportunity employer. We’re committed to fair hiring practices and a welcoming working environment. All candidates are considered for employment without regard to race, religion, ethnicity, age, gender, sexual identity or expression, medical condition, or socioeconomic status. We value our differences and we’re excited to learn what you can add to our team.
Vicarious is developing artificial general intelligence for robots. By combining insights from generative probabilistic models and systems neuroscience, our architecture trains faster, adapts more readily, and generalizes more broadly than AI approaches commonly used today.