Key skills
AWS KubernetesAWS EKSAWS ECSAWS LambdaAWS EC2PythonInfrastructure-as-CodeTerraformAWS CDKAWS S3AWS EFSAWS FSxCI/CD pipelinesInfrastructure securityAWS IAMVaultKMSInfrastructure observabilityGrafanaPrometheusDatadogGPU instance managementTraining cluster optimizationBatch inference pipelinesCloud cost optimizationAIArtificial IntelligenceMachine LearningMLAWSKubernetesECSEKSEC2LambdaS3IAMCI/CDCloud Security
About this role
ArteraAI is an AI startup focused on developing medical artificial intelligence tests for cancer therapy personalization. As a Software Engineer on the Platform Engineering team, you will collaborate with various teams to design and maintain the infrastructure that supports Artera's AI products.
Responsibilities:
- Design, build, and maintain compute infrastructure programmatically (AWS Kubernetes/EKS, AWS ECS, Lambda, and EC2) that powers Artera's AI products at scale
- Work closely with stakeholders to define and refine the platform's architecture, ensuring scalability, observability, reliability, and performance
- Build out core infrastructure, tooling, and software development processes
- Work closely with machine learning engineers to optimize training and inference workflows with efficiency and cost in mind
- Contribute to a range of platform engineering projects, from one-off solutions to long-term systems
- Contribute to cloud infrastructure security tools and services
Requirements:
- 2+ years managing containerized infrastructure at scale with a strong security mindset
- 3+ years building services and tools using Python with a software engineering mindset
- 2+ years building infrastructure automation solutions using Infrastructure-as-Code (e.g., Terraform, AWS CDK)
- Experience with AWS storage services: S3, EFS, FSx
- Experience with CI/CD pipelines
- Demonstrated integrity and consideration for appropriate data governance when working with sensitive, confidential data
- This is a remote role open to candidates who are currently authorized to work either in the United States or in Canada without the need for current or future employment-based visa sponsorship
- Experience with infrastructure observability and monitoring tools (e.g., Grafana, Prometheus, Datadog)
- Experience supporting ML/AI workloads — GPU instance management, training cluster optimization, batch inference pipelines
- Familiarity with cost optimization strategies for cloud compute at scale
- Experience with secrets management and cloud security tooling (e.g., AWS IAM, Vault, KMS)
- Contributions to internal developer tooling or platform libraries consumed by cross-functional teams