Scale AI Staff Software Engineer at Scale AI develops core product building blocks for agentic capabilities in the public sector. Responsibilities include orchestrating feature implementation, defining technical strategy, and mentoring engineers.
Responsibilities
Define technical strategy for agentic guardrails, explainability, and fleet orchestration.
Ensure system reliability and performance across multiple security classifications and network types.
Mentor engineers in the process of defining requirements with stakeholders and gathering acceptance.
Communicate high-level technical trade-offs and implementation strategies to senior government stakeholders and Scale C-Suite members.
Influence the long-term product strategy and technical roadmap for the Federal business unit.
Consult on the architecture of AI-powered solutions for large-scale federal contracts.
Ideally you will have:
Full Stack Development: Proficiency in front-end, back-end development and infrastructure, including experience with modern web development frameworks, programming languages, and databases
Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus
Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles
AI Application Integration: Familiarity with integrating Large Language Models (LLMs) and building agentic workflows. Understanding of prompt engineering, retrieval-augmented generation (RAG), and agent orchestration is beneficial.
Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles
Qualification
Proficiency in front-endFamiliarity with cloud platforms (eUnderstanding of data modelingUnderstanding of containerization (eUnderstanding of prompt engineering
Required
Proficiency in front-end, back-end development and infrastructure
Experience with modern web development frameworks, programming languages, and databases
Familiarity with cloud platforms (e.g., AWS, Azure, GCP)
Experience in developing and deploying applications in a cloud-native environment
Knowledge of ETL processes and experience in building data pipelines
Understanding of data modeling, data warehousing, and data governance principles
Familiarity with integrating Large Language Models and building agentic workflows
Preferred
Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes)
Understanding of prompt engineering, retrieval-augmented generation (RAG), and agent orchestration
