6 months ago
Base Salary
$194k - $311k/yr
Responsibilities
- Lead the design and implementation of scalable backend systems and distributed architectures for federal customers
- Manage the full feature lifecycle from requirements definition through deployment on classified networks and stakeholder acceptance
- Direct orchestration of asynchronous agent fleets to meet mission requirements
- Lead customer engagements and translate mission needs into technical requirements
- Own stakeholder communication and ensure implementations meet acceptance criteria
- Conduct technical reviews and identify risks in machine learning infrastructure and model serving
- Drive the platform roadmap by providing technical specifications for federal product offerings
Requirements
- Proficiency in front-end, back-end, and infrastructure development using modern web development frameworks, programming languages, and databases
- Familiarity with AWS, Azure, or GCP and cloud-native application development
- Understanding of Docker and Kubernetes is a plus
- Knowledge of ETL processes, data pipelines, data modeling, data warehousing, and data governance
- Familiarity with LLM integration, agentic workflows, prompt engineering, retrieval-augmented generation, and agent orchestration is beneficial
- Strong analytical, problem-solving, collaboration, communication, adaptability, and learning agility skills
Benefits
- Comprehensive health, dental, and vision coverage
- Retirement benefits
- Learning and development stipend
- Generous paid time off
- Potential commuter stipend
- Full-time role with location-specific work in San Francisco, New York, Seattle, Hawaii, Washington DC, Texas, Colorado, or St. Louis
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.