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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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As a Senior Software Engineer at Coder, you’ll build and evolve the systems behind our agentic development experience. You’ll work across the agent ha...
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As an Engineering Manager on Coder’s Agentic Engineering team, you’ll lead engineers building and evolving the systems behind our agentic development...
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As a Staff Software Engineer on Coder’s Agentic Engineering team, you’ll shape the systems behind our agentic development experience. You’ll work acro...
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We are looking for a Senior Partner Deployed Engineer to join the Customer Solutions team, focused on building Coder’s partner ecosystem across EMEA....
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OpenRouter is the leading AI routing and infrastructure layer that enterprises use to access, manage, and optimize the best large language models acro...
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As a Senior Software Engineer on Coder’s Engineering team, you’ll build and evolve the systems behind our agentic development experience. You’ll work...
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Launched in 2019, Constructor is an AI-first ecommerce search and discovery platform that helps shoppers find the right products at the right time and...
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Posted 4 days ago
Senior Backend Engineer: Machine Learning Infrastructure
Constructor
Remote - EMEA
Launched in 2019, Constructor is an AI-first e-commerce search and discovery platform that helps shoppers find the right products at the right time an...
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On this journey for over 12 years, Qventus is leading the transformation of healthcare. We enable hospitals to focus on what matters most: patient car...
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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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