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Browse remote CI/CD positions — ML pipeline automation, DevOps for AI systems, and deployment engineering roles at AI-first companies.
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At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developer...
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At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developer...
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At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developer...
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Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, R...
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We are an applied AI lab building end-to-end software agents. We're the makers of Devin, the first AI software engineer. Our team is extremely talent-...
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At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developer...
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We are an applied AI lab building end-to-end software agents. We're the makers of Devin, the first AI software engineer. Our team is extremely talent-...
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We are an applied AI lab building end-to-end software agents. We're the makers of Devin, the first AI software engineer. Our team is extremely talent-...
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Posted 2 months ago
Member of Technical Staff (Offensive Security Engineer)
Perplexity
San Francisco
Perplexity is seeking a highly skilled, experienced and hands-on Offensive Security Engineer to join our dynamic security team, taking an adversarial...
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Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writ...
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Role guide
What to expect in remote CI/CD jobs
CI/CD for AI is more complex than standard software — it requires validating not just code, but data, models, and system behaviour. Remote CI/CD jobs at AI companies involve building automated testing and deployment pipelines for ML systems, implementing model regression testing, and ensuring AI products ship reliably. Engineers who bridge DevOps and ML system knowledge are in consistent demand.
What you'll work on
- Build automated ML testing pipelines that prevent broken models reaching production
- Design deployment workflows that ship AI updates in minutes, not days
- Implement shadow mode testing to safely validate new model versions on live traffic
- Create infrastructure-as-code environments for reproducible AI development
What companies look for
- CI/CD platform experience — GitHub Actions, GitLab CI, CircleCI, or Jenkins
- ML-specific testing — data validation, model regression, integration testing
- Blue/green and canary deployment strategies for safe ML model releases
Frequently asked questions
What remote CI/CD jobs are available at AI companies?
MLOps and DevOps engineering roles where CI/CD for ML systems is a core responsibility. Most require Docker and cloud platform experience (AWS, GCP, or Azure) alongside CI/CD pipeline knowledge.
What makes CI/CD for ML different from regular software CI/CD?
ML pipelines must validate data quality, model performance metrics, and integration behaviour — not just code correctness. Engineers design test suites that catch data drift, model degradation, and integration failures automatically.
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