2 months ago
Base Salary
$138k - $293k/yr
Responsibilities
- Embed with government customers to understand missions, scope problems, and develop working software.
- Own end-to-end delivery by designing, building, deploying, and maintaining solutions from prototype through stable production.
- Build backend systems and data integrations connecting customer environments with Scale’s platform.
- Collaborate with product and engineering teams to turn field patterns into reusable platform capabilities.
- Serve as the technical point of contact for customer stakeholders and guide adoption.
Requirements
- 5+ years of software engineering experience, preferably in customer-facing, forward-deployed, or government environments.
- Strong production coding skills in Python and TypeScript or comparable modern languages.
- Experience with cloud platforms such as AWS, Azure, or GCP, plus 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 or eligibility to obtain one.
- Preferred experience building or deploying LLM- or generative-model-powered systems.
- Preferred familiarity with Node, React, Kubernetes, Docker, Terraform, ETL pipelines, data modeling, and data governance.
Benefits
- Hybrid work arrangement with significant customer-site presence.
- Approximately 25% travel, with up to 50% travel as necessary.
- Possible relocation to Washington, DC, or St. Louis.
- Comprehensive health, dental, and vision coverage.
- Retirement benefits, learning and development stipend, generous paid time off, and potentially a commuter stipend.
- Eligible roles may include equity compensation.
Categories
Forward 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.