2 months ago
Base Salary
$186k - $279k/yr
Responsibilities
- Embed with government customers to understand missions, scope problems, and turn requirements into working software.
- Design, build, deploy, and maintain solutions at customer sites from prototype through stable production.
- Build backend systems and data integrations connecting customer environments with Scale's platform.
- Feed field insights into product and engineering roadmaps to create reusable platform capabilities.
- Serve as the technical point of contact for customer stakeholders and guide adoption.
- Work on-site with customers and travel approximately 25%, with up to 50% travel as needed.
Requirements
- At least 5 years of software engineering experience, ideally in customer-facing, forward-deployed, or government contexts.
- Strong production coding skills in Python and TypeScript or comparable modern languages.
- Experience across the stack with cloud platforms, containers, Linux, and networking fundamentals.
- Ability to work independently in ambiguous, fast-moving environments and make sound trade-offs under pressure.
- Active TS/SCI clearance is required.
- Experience building or deploying systems powered by LLMs or generative models is a bonus.
- Familiarity with Node, React, Kubernetes, Docker, and Terraform is a bonus.
- Data engineering experience with ETL pipelines, data modeling, and data governance is a bonus.
Benefits
- Hybrid customer-facing role with significant on-site customer time
- Approximately 25% travel, with up to 50% travel as necessary
- Relocation opportunity to Washington, DC or St. Louis
- Comprehensive health, dental, and vision coverage
- Retirement benefits
- Learning and development stipend
- Generous paid time off
- Potential commuter stipend
Categories
BackendForward Deployed
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.