1 year ago
Base Salary
$188k - $235k/yr
Responsibilities
- Define architectural patterns for secure, reliable, and scalable multi-cloud infrastructure supporting enterprise agentic workflows.
- Lead the infrastructure roadmap with emphasis on compliance, privacy, security, change management, and data isolation.
- Own development and maintenance of the agentic observability platform covering logging, metrics, tracing, and analytics.
- Build automated developer tooling and promote Infrastructure-as-Code practices across engineering.
- Solve large-scale multi-tenancy, data-isolation, and high-performance inference challenges with end-to-end ownership across the product lifecycle.
Requirements
- At least five years of full-time software engineering experience in a senior role.
- Deep understanding of Infrastructure-as-Code, container orchestration, observability platforms, and modern infrastructure practices.
- Extensive experience with at least one major cloud provider: AWS, Azure, or GCP.
- Strong knowledge of enterprise security and compliance, including access management, data isolation, and customer-specific VPC configurations.
- Proficiency in Python or JavaScript/TypeScript and SQL.
- Hands-on experience or passion for agents, LLMs, vector databases, and emerging AI technologies is preferred.
Benefits
- Comprehensive health, dental, and vision coverage.
- Retirement benefits, learning and development stipend, generous paid time off, and potentially a commuter stipend.
- Full-time position based in San Francisco, New York, or Seattle; salary location may affect compensation.
- The company requires a 90-day waiting period before reconsidering candidates for the same role.
Tech Stack
AWSAzureDatadogGoogle Cloud PlatformGrafanaJavaScriptKubernetesPrometheusPythonSQLTerraformTypeScript
Categories
About Scale AI
Scale AI builds data annotation services and AI development tools for enterprises and government agencies, sold as a platform and managed services. Its products include the Scale Generative AI Platform for building and evaluating agents and the Data Engine for collecting, curating, and labeling training data, including RLHF and model evaluation. Founded in 2016 and headquartered in San Francisco, the company is privately held and works across domains from computer vision to LLM applications.