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Staff / Senior Software Engineer (Back-end, SaaS, Python)

DataChain, Inc.· Engineering – Studio
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RemoteFull Time, RemotePosted 11mo ago

About the role

Seeking a Python backend engineer to work on iterative.ai's SaaS offering 'Studio', focusing on ML workspace collaboration, with responsibilities in full development lifecycle, code quality, and team leadership.

Responsibilities

  • Discuss and research issues, bugs and new features.
  • Take ownership of the full development lifecycle - From planning, through designing, execution, testing to supporting in production.
  • Be 100% hands-on - we expect technical expertise on the backend and some full stack ability and involvement in the frontend (react, typescript) to be able to be effective.
  • Document your code and share knowledge with the team.

Qualifications

  • Motivation and interest (Technologies, domain, customer impact).
  • Love for clean & efficient code.
  • Remote work self-discipline - ability to focus and deliver reliably.
  • Excellent communication skills - clear, constructive, and respectful dialog.
  • Technical Leadership - hands-on technical leadership, experience in leading big features and architectural decisions.

Skills

Required:PythonFull-stack developmentCode design and standards

Benefits

  • Work wherever you want - No offices, distributed remote team.
  • Work whenever you want - Asynchronous culture, flexible scheduling, unlimited PTO.
  • Open-source your code - Visibility and usage by thousands of developers.
  • Competitive compensation based on your work.
  • Great health coverage (medical, dental, vision) for you and your family, fully paid (US).
  • 401K with 100% match up to 4% (US) - other regions may have reimbursements.
  • Participation in conferences and community events.
Last updated Feb 6, 2026. Interested? Apply on the company site.
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About DataChain, Inc.

The Fastest Way to Make Multimodal Data AI-Ready

Discover DataChain products and services. DataChain builds a suite of tools for data preprocessing and management, experiment tracking, ML models versioning, and pipeline automation.

Data ManagementExperiment TrackingML Models VersioningPipeline Automation
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