Tech Lead, Autonomy Systems

1XSan Carlos, CA
$199,763 - $350,000Remote

About The Position

Train machine learning models for robotic navigation, manipulation, and locomotion. Implement infrastructure to scale up the data engine’s ability to train on more data and more compute. Develop algorithmic advances to improve our models' sample efficiency and generalization capability. Work with the data collection team to improve their efficiency and evaluation processes. Interview hiring candidates for the AI team. Working with the software team to improve calibration and timing correctness of the robotics stack. Speeding up the data loader to increase training speeds. Implement data inspection tools to interpret AI models better. International travel required, 20%.

Requirements

  • Master’s degree or foreign degree equivalent in Computer Science or equivalent or related field and three (3) years of experience in the job offered or related role.
  • Experience and/or education must include: Writing software to control robots for inverse kinematics, control regimes, camera calibration and linear algebra.
  • Experience and/or education must include: Machine Learning, training neural networks to automate various tasks using humanoid robots.
  • Experience and/or education must include: Coding and Software Engineering, training neural networks.
  • Experience and/or education must include: Writing Python programming.
  • Experience and/or education must include: GPU programming, Computer Vision, and Deep Learning.
  • Experience and/or education must include: Open-ended research, Natural Language Processing, and Linear Algebra.

Responsibilities

  • Train machine learning models for robotic navigation, manipulation, and locomotion.
  • Implement infrastructure to scale up the data engine’s ability to train on more data and more compute.
  • Develop algorithmic advances to improve our models' sample efficiency and generalization capability.
  • Work with the data collection team to improve their efficiency and evaluation processes.
  • Interview hiring candidates for the AI team.
  • Work with the software team to improve calibration and timing correctness of the robotics stack.
  • Speed up the data loader to increase training speeds.
  • Implement data inspection tools to interpret AI models better.
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