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Remote CI/CD Jobs
Browse remote CI/CD positions — ML pipeline automation, DevOps for AI systems, and deployment engineering roles at AI-first companies.
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Security is at the foundation of OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity. The Security team protects...
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Security is at the foundation of OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity. The Security team protects...
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Security is at the foundation of OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity. The Security team protects...
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Posted 5 months ago
Applications closedML Platform Engineer - Forecasting Infrastructure
Kvasir AI
Remote
Los Angeles, United States (UTC-8)
The forecasting team has good ideas and not enough platform leverage. Training runs are inconsistent, feature pipelines are fragile, and deployment ha...
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Posted 5 months ago
Applications closedSenior AI Platform Engineer - Model Reliability
Luminary AI
Remote
New York, United States (UTC-5)
This is the role for someone who is a little tired of hearing about AI strategy and a lot more interested in why the deployment failed at 2:13 a.m. Ou...
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Posted 5 months ago
Applications closedSenior MLOps Consultant - 12 Week Migration
PolyLingua AI
Remote
New York, United States (UTC-5)
We need a contractor who can come in, assess a messy but functioning ML stack, and help us move it onto more reliable infrastructure without turning t...
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Posted 5 months ago
Applications closedPlatform Engineer - ML Training Pipelines
Solaria Data
Hybrid (Berlin, DE)
Berlin, Germany (UTC+1)
Our research and applied ML teams move quickly, but the training stack around them is inconsistent and expensive. We need a platform engineer to stand...
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Posted 5 months ago
Applications closedStaff MLOps Engineer - Clinical Documentation AI
PolarML
Remote
New York, United States (UTC-5)
We build software used by clinical operations teams to review, summarise, and route visit documentation. The models are important, but reliability is...
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Posted 5 months ago
Applications closedSenior MLOps Engineer — AI Observability
Synaptic Labs
Remote
New York, United States (UTC-5)
About the role: we are hiring a Senior MLOps Engineer to join the core platform team behind our AI observability product. Our customers run production...
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Posted 6 months ago
Applications closedML Platform Engineer
Riviera Health AI
Hybrid (Boston, US)
New York, United States (UTC-5)
ML Platform Engineer needed for a biotech software environment supporting molecular diagnostics workflows. The platform needs stronger standardisation...
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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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