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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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ClickHouse is the open-source, real-time analytical database behind a lot of today's AI. More than 4,000 companies build on ClickHouse, including Anth...
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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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We're looking for a Solutions Architect to serve as the primary technical partner for our prospects and customers. You'll work hand-in-hand with Sales...
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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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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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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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The Cloud Scaling team is dedicated to implementing robust capabilities within the ClickHouse cloud environment. We seek exceptional software engineer...
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Posted 3 weeks ago
Senior Cloud Infrastructure Engineer - Cloud Foundations
Lambda
San Francisco Office (Fremont St)
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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The Cloud Scaling team is dedicated to implementing robust capabilities within the ClickHouse cloud environment. We seek exceptional software engineer...
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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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