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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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Posted 2 weeks ago
Technical Product Manager – Storage
Nebius
Amsterdam, Netherlands; Remote - Europe
Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers...
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Skydio is the leading US drone company and the world leader in autonomous flight, the key technology for the future of drones and aerial mobility. The...
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We are rebuilding biotech for the AI era. When a breakthrough is delayed, the world waits. Getting a molecule from discovery to patients, or a crop fr...
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Skydio is the leading US drone company and the world leader in autonomous flight, the key technology for the future of drones and aerial mobility. The...
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Posted 3 weeks ago
Senior Technical Product Manager - AI Compute Platform
Nebius
Amsterdam, Netherlands; Remote - Europe
Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers...
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Build AI contracting at enterprise scale. Ship it fast. Ironclad is building AI on one of the most consequential datasets in enterprise software: over...
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Skydio is the leading US drone company and the world leader in autonomous flight, the key technology for the future of drones and aerial mobility. The...
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Ema builds AI Employees for HR, IT and Finance. Our AI Employees take on the busy work across the employee experience, from recruiting, onboarding and...
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turbopuffer is looking for someone to build our partner program from scratch — the strategy, the integrations, and the first real revenue through it....
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Elicit is an AI research assistant that uses language models to help researchers figure out what’s true and make better decisions, starting with commo...
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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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