Datology AI
Software Engineer, Cloud Infrastructure
About this role
DatologyAI seeks a Cloud Infrastructure Engineer to design and operate scalable, secure cloud systems supporting AI training and data curation pipelines. You'll architect multi-cloud infrastructure, manage Kubernetes deployments, and optimize CI/CD processes while collaborating with research and engineering teams at a well-funded AI startup.
What you'll do
- Architect and maintain multi-cloud infrastructure on AWS with focus on reliability and scalability
- Define infrastructure-as-code practices using Terraform, CloudFormation, and similar tools
- Design and manage Kubernetes systems for model training, inference, and data processing
- Build monitoring, alerting, and logging systems for high availability and observability
- Optimize CI/CD pipelines and streamline service deployments across environments
- Support large-scale ML model training and ensure infrastructure for hybrid/on-prem deployments
What they're looking for
- AWS and cloud infrastructure (4+ years)
- Kubernetes and containerization
- Terraform and infrastructure-as-code
- Systems-level debugging and networking
- Bash, Python, or Go scripting
- CI/CD pipeline optimization
- ML workload infrastructure (nice-to-have)
- Cost optimization and monitoring
Benefits
- Based in Redwood City, CA with 4 days in-office
- Early-stage opportunity with deep technical and cultural impact
- Well-funded startup ($57.5M raised) with prominent investors and advisors
- Collaborate with cutting-edge AI research team
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Datology AI
DatologyAI builds a data curation platform that optimizes AI training datasets to reduce costs and improve model performance. The company is hiring cloud infrastructure engineers, data platform engineers, full-stack product engineers, and solutions engineers to scale its multi-cloud infrastructure and customer-facing tools.
View all jobs at Datology AILikely interview questions
- Walk us through a time you architected multi-cloud or hybrid-cloud infrastructure from scratch. What challenges did you face and how did you solve them?
- Describe your experience with Kubernetes in production. How have you handled scaling, resource management, or incident response in a Kubernetes environment?