6 months ago
Base Salary
$138k - $259k/yr
Responsibilities
- Build and maintain internal CLI and tooling for standardized testing, deployment, and environment management.
- Design and implement secure, scalable backend systems for public-sector customers.
- Own the model inference layer, including networking, latency debugging, and AI model pricing and usage metrics.
- Own services and systems, define long-term health goals, and improve the health of surrounding components.
- Build integration tests with Product to identify failures upstream and reduce infrastructure-only debugging.
- Collaborate with cross-functional teams and customers to define and deliver backend solutions for secure government environments.
- Contribute to the platform roadmap and product strategy for Scale AI’s Public Sector business.
- Improve engineering standards, tooling, and processes.
Requirements
- Active security clearance and ability to obtain a TS/SCI with CI Poly are required.
- Proficiency in both front-end and back-end development, including modern web frameworks, programming languages, and databases.
- Experience developing and delivering software to air-gapped and isolated environments is a plus.
- Understanding of Docker, Kubernetes, and cloud platforms such as AWS, Azure, or GCP is desired.
- Experience developing software that meets strict federal security, regulatory, and compliance requirements.
- Experience with federal compliance frameworks and requirements such as Cloud SRG, FedRAMP, and STIG Benchmarks.
- Strong analytical, problem-solving, collaboration, and communication skills.
- Ability to support work from the DC, SF, NYC, or STL office 3–4 days per week.
Benefits
- Comprehensive health, dental, and vision coverage
- Retirement benefits
- Learning and development stipend
- Generous paid time off
- Commuter stipend may be available
- Hybrid schedule requiring work from the DC, SF, NYC, or STL office 3–4 days per week
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.