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Browse remote AWS positions at AI companies — SageMaker ML platform engineering, cloud AI infrastructure, and MLOps on AWS.
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Hex is growing our team of builders on a mission to make everyone a data person. Our platform solves key pain points with today’s data and analytics t...
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Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows,...
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We are growing our support team at ClickHouse, and we're looking for a Senior Technical Customer Support Engineer, Federal, to serve the specific need...
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Posted 3 weeks ago
Member of Technical Staff, Cloud & Infrastructure Security
Fireworks AI
San Mateo
Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows,...
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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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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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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 Security Team is responsible for providing key security capabilities covering application, cloud and enterprise security, incident response, detec...
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Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows,...
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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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Role guide
What to expect in remote AWS jobs
AWS is the most widely used cloud platform for AI companies. Remote AWS jobs in this space combine cloud engineering with ML infrastructure knowledge — SageMaker for model training and deployment, S3 and Glue for data pipelines, and Lambda or ECS for serving. Engineers who bridge cloud architecture and ML system design are consistently in demand.
What you'll work on
- Deploy and monitor ML models using SageMaker endpoints and pipelines
- Build scalable data ingestion and processing pipelines with S3 and Glue
- Design cost-efficient ML training infrastructure on EC2 GPU instances
- Create serverless AI feature APIs using Lambda and API Gateway
What companies look for
- SageMaker experience — training jobs, endpoints, and pipelines
- Strong AWS fundamentals — IAM, VPC, S3, EC2, ECS, Lambda
- Infrastructure-as-code with Terraform or CDK
Frequently asked questions
What AWS jobs are available remotely at AI companies?
Cloud engineering roles focused on ML infrastructure — SageMaker pipelines, data lake architecture on S3, and AI service integration. Many also involve ECS or EKS for container orchestration.
Do remote AWS AI jobs require SageMaker experience?
For ML-focused roles, SageMaker experience is a strong advantage. General cloud engineering roles may focus on ECS/EKS, Lambda, or data services instead. Check listings for the specific service stack.
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