Neuralink

Neuralink

Machine Learning Engineer Intern

South San Francisco, California, United States · Intern · Internship

Sponsorship not specifiedDetected 29 days ago
Machine LearningCommunicationCollaboration

About the role

  • These devices allow us to restore movement to the paralyzed, restore sight to the blind, and revolutionize how humans interact with their digital world.
  • Furthermore, the team is focused on restoring speech for mute individuals and enabling direct, natural silent communication with AI agents.

Responsibilities

  • You'll work with cross-functional teams to design new BCI functionalities and novel computer user interfaces.

Requirements

  • Experience in analyzing complex datasets, driving insights, and communicating results in a simple and clear way to both technical and non-technical stakeholders

Nice to have

  • Experience working with time series or unstructured data
  • Expected Compensation:
  • The anticipated hourly rate for this position is listed below.
  • California Hourly Rate:

Compensation

  • The anticipated hourly rate for this position is listed below.
  • California Hourly Rate:
  • What We Offer:
  • An opportunity to change the world and work with some of the smartest and most talented experts from different fields
  • Growth potential; we rapidly advance team members who have an outsized impact
  • Meals provided

Benefits

  • Excellent medical, dental, and vision insurance through a PPO plan
  • Commuter benefits
  • Equity (RSUs) *Temporary Employees & Interns excluded
  • Parental leave *Temporary Employees & Interns excluded
  • Flexible time off *Temporary Employees & Interns excluded
  • Full-time employees are eligible for the following benefits listed below.
  • We are hiring a Machine Learning Engineer Intern to develop novel neural decoders to increase control speed and accuracy, improve reliability, and expand functionality of BCIs.

Company info

  • We are creating devices that enable a bi-directional interface with the brain.

This listing is sourced directly from Neuralink's careers page and normalized into a canonical job model.