Research Robotics/Computer Vision Engineer

Responsibilities

• (i) implementing perception on robots to enable safe exploration and navigation in real world environments in collaboration with the locomotion team
• (ii) reconstructing an entire scene in 3D using monocular images, estimating camera poses, optimizing and streamlining 3D SLAM
• (iii) developing a set of software tools for localization of a robot using only visual inputs
• (iv) building robust software to enable life-long mapping on a robot via optimally merged pose-graphs
• (v) visual servoing wrt objects detected/ tracked to control robot motion
• (vi) researching novel techniques to detect and cater to glare during robotic mapping and navigation
• (vii) building infrastructure and pipeline and collecting data to enable streaming of hand movements for training robot manipulation tasks such as pick and place
• (viii) maintaining a camera and 2D lidar based navigation stack, including fixing bugs, adding new customer feature requests, and ensuring successful deployments.

Requirements

• Must have a master’s degree (or foreign equivalent) in Computer Vision, Robotics, or a directly related discipline and one (1) year of experience in Machine Learning or Data Science.
• Must have any experience with or knowledge of each of the following: (i) reconstructing 3D scenes using monocular videos, meshes, pointclouds, Neural Radiance Fields, and Gaussian Splats; (ii) reconstructing rigid and articulated hand-held objects from videos, including inferring the time-varying hand configurations and relative poses of the objects; (iii) using generative computer vision, including diffusion models to guide reconstruction, or addressing occlusion and limited viewpoint variations in videos via data driven priors; (iv) optimizing attention-based models for perception used in autonomous navigation systems; (v) using Neural Architectural Search (NAS) to find better perception backbone architectures with higher accuracies and lower latencies; and (vi) cloud-based training in AWS, Google cloud, or Vetex AI and optimized data loading for cloud based distributed training for deep learning workloads (e.g. Pytorch dataloader, or sharding) with hardware-in-loop.

Pay

Base Salary Range: $250,000 - $300,000 USD