Software Engineer, Cloud Infrastructure

DatologyAI

. Architect and maintain multi-cloud infrastructure, primarily AWS and potentially Azure/GCP .

Posted 10/16/2026full-timeSan Mateo • California • United StatesMid-LevelSenior💰 $180,000 - $300,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and maintaining multi-cloud infrastructure, particularly with AWS, while implementing infrastructure-as-code practices using Terraform and Kubernetes. Capable of optimizing CI/CD pipelines and ensuring high availability through effective monitoring and incident response.

Highest-signal resume keywords

AWS Cloud InfrastructureTerraform Infrastructure-as-CodeKubernetes ManagementCI/CD Pipeline OptimizationSystems-Level Debugging

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Infrastructure-as-CodeKubernetesTerraformBash ScriptingPython ScriptingGo ProgrammingMulti-Cloud ArchitectureCI/CD OptimizationMonitoring and Logging SystemsCost-Efficiency Strategies

Tools & Technologies

AWSAzureGCPCloudFormationPulumiCI/CD ToolsMonitoring ToolsAlerting SystemsLogging SystemsGPU Orchestration

Industry Keywords

Multi-CloudHybrid CloudMachine Learning WorkloadsEnterprise DeploymentsComplianceSecurityInfrastructure SystemsStartup EnvironmentData Processing WorkloadsIncident Response

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformKubernetesPythonTerraformGo

About the role

Key responsibilities & impact

Architect and maintain multi-cloud infrastructure, primarily AWS and potentially Azure/GCP
Define and implement infrastructure-as-code best practices using Terraform, CloudFormation, Pulumi, and similar technologies
Design and manage Kubernetes-based systems for model training, inference, and data processing workloads
Optimize CI/CD pipelines and streamline service deployments across environments
Build monitoring, alerting, and logging systems for high availability and observability
Collaborate with research and engineering teams to support training large-scale ML models
Ensure infrastructure supports cloud, on-prem, and hybrid deployment models for enterprise use cases
Drive cost-efficiency strategies across compute and storage resources
Respond to and resolve infrastructure-related incidents
Work with engineering, research, and product teams to define deployment and compute-resource management across AWS and other cloud providers

Requirements

What you’ll need

4+ years of relevant experience
Experience leading or helping build robust infrastructure systems at a startup or fast-moving engineering organization
Deep experience with cloud providers, especially AWS
Exposure to multi-cloud or hybrid-cloud setups
Strong Kubernetes, Terraform, and containerized architecture skills
Systems-level debugging experience, including networking issues, memory leaks, and resource bottlenecks
Ability to write clean, maintainable scripts in Bash, Python, or Go
Experience supporting infrastructure for ML workloads, training pipelines, inference clusters, or GPU orchestration (nice to have)
Experience building or scaling infrastructure for large-scale datasets (nice to have)
Exposure to cloud cost monitoring and optimization tools (nice to have)
Background supporting compliance and security in enterprise deployments (nice to have)

Benefits

Comp & perks

100% covered health benefits (medical, vision, and dental)
401(k) plan with a generous 4% company match
Unlimited PTO policy
Paid Parental Leave of 12 weeks, plus 6 months of WFH flexibility
Annual $2,000 wellness stipend
Annual $1,000 learning and development stipend
Daily lunches and snacks are provided in our office
Relocation assistance for employees moving to the Bay Area
Meaningful equity