Job role insights

  • Date posted

    May 26, 2026

  • Closing date

    May 26, 2026

  • Offered salary

    Negotiable Price

  • Career level

    Middle Senior

  • Experience

    10+ Years 3 - 5 Years 6 - 9 Years

Description

Company: OpenMined

Job Category: Software Engineering / Privacy Tech / AI

Contract Type: Full-Time, Permanent

Location Eligibility: Remote — USA, Mexico, Canada, Colombia, Brazil, Argentina

Salary: Competitive (nonprofit-adjusted; commensurate with experience)

Application Link: https://apply.workable.com/openmined/j/0C52D130E3/

Application Email: N/A Closing Date: Open until filled

About the Company: OpenMined is a nonprofit on a mission to build the public network for the world's non-public data — unlocking up to 1,000,000x more data for machine learning research by making it safe and private to use. They build open-source tools for privacy-preserving AI including PySyft and other federated learning frameworks.

Job Description: OpenMined is seeking a Senior/Staff Software Engineer to help build the infrastructure for private data access at scale. You will work on core platform components that enable privacy-preserving computation and federated data workflows.

Key Responsibilities:

  • Architect, build, and maintain core platform components for privacy-preserving AI
  • Contribute to PySyft and OpenMined's open-source ecosystem
  • Collaborate with researchers and ML engineers to build scalable tooling
  • Improve developer experience for data owners and data scientists using the platform
  • Work in a mission-driven, async-first global team

Requirements:

  • Demonstrated proficiency in Python (strongly preferred)
  • Experience with distributed systems and large-scale software architecture
  • Passion for privacy, open-source, and responsible AI
  • Ability to work effectively in remote, async environments
  • Experience with ML frameworks or privacy-preserving computation a strong plus

Skills Required: Python, distributed systems, open-source contribution, privacy-preserving ML, federated learning, async communication

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