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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 Zencoder.ai, we build and orchestrate AI agents that ship real work - code, research, operations, and more. What started as developer tooling is be...
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At Zencoder.ai, we build and orchestrate AI agents that ship real work - code, research, operations, and more. What started as developer tooling is be...
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Warp: We're Building the Platform for Agentic Development Warp began with the vision of reimagining one of the fundamental dev tools, the terminal, to...
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Dropzone’s mission is to scale cybersecurity beyond human limits, and augment every single human security engineer/analyst with an army of AI security...
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Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training...
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Runpod is the AI Developer Cloud. More than one million developers, from indie researchers to teams running frontier models in production, use Runpod...
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Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, St...
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The Control Plane Platform team is building the shared foundation that every product in ClickHouse Cloud is built and shipped on. We give ClickHouse e...
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Posted 1 week ago
DevOps Engineer
Vannevar Labs
Remote; Washington, District of Columbia, United States
Vannevar builds AI systems for the Department of War's most consequential missions. We have 125 deployments across every branch and combatant command...
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Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI resear...
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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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