Software Engineer, Research Infrastructure (University Grad) at Meta
Redmond, Washington, USA -
Full Time


Start Date

Immediate

Expiry Date

08 Oct, 25

Salary

56.25

Posted On

08 Jul, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Python, Computer Science, Computer Engineering, Scripting Languages, Javascript, Programming Languages

Industry

Computer Software/Engineering

Description

We are looking for a skilled backend developer to join our team, whose mission is to develop custom infrastructure in close collaboration with researchers and engineering team in order to support bleeding edge AI, haptics research, and broad-coverage models of human perception and perceptually-driven outcomes. We are specifically looking for someone interested in collaborating with scientists and building scalable and robust services. More broadly, you will work with a highly diverse and interdisciplinary team of researchers and engineers and will have access to cutting edge technology, resources, and testing facilities.

MINIMUM QUALIFICATIONS:

  • Currently has, or is in the process of obtaining a Bachelor’s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
  • Familiarity with at least one of the following programming languages: Python, JavaScript, or similar scripting languages
  • Experience working in a source-controlled environment or in scalable systems
  • Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment

PREFERRED QUALIFICATIONS:

  • Familiarity with working in a research environment or with non-technical colleagues
  • Experience working and communicating cross-functionally in a team environment
  • Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)
Responsibilities
  • Build reliable and reusable software tools and libraries to accelerate research and integration programs
  • Develop custom ML compute infrastructure using tools like Kubernetes, Docker, Python, as well as a variety of Meta-specific technologies
  • Simplify access to best practices in software and machine learning
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