Software Engineer, Public Sector
USD per year
Software Engineer, Public Sector
San Francisco, CA; St. Louis, MO; New York, NY; Washington, DC Join the team shaping the future of AI at Scale. Apply Now The Public Sector software engineers (SWEs) create the core product building blocks forward-deployed teams use to develop agentic capabilities that function across multiple domains. SWEs responsibilities include building the systems required to ingest and process federal datasets to support real-time decision-making in contested environments. We develop novel agentic enabling capabilities that includes:
- Create multi-layered guardrails around agents
- Optimize data retrieval for agents
- Orchestrate fleets of asynchronous agents
- Automatically alerts users to deviations in data
- Illustrating how an agent reached a decision
As a Software Engineer, you will own the development of a vertical feature or a horizontal capability to include defining requirements with stakeholders and implementation until it is accepted by the stakeholders. You will:
- Design and implement scalable backend systems for Federal customers using cloud-native AI infrastructure.
- Build features for agentic systems including multi-layered guardrails and data retrieval optimization.
- Develop data pipelines and machine learning infrastructure to make data sources accessible by agents.
- Collaborate with cross-functional teams to execute backend solutions for secure environments.
- Participate in customer engagements to understand requirements and deliver technical solutions.
- Define requirements with stakeholders and implement features until they are accepted.
- Contribute to the platform roadmap and product strategy for the Federal business.
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
- Collaboration and Communication: Excellent interpersonal and communication skills to effectively collaborate with cross-functional teams, stakeholders, and customers. Ability to clearly articulate technical concepts to non-technical audiences and foster a collaborative work environment
- Adaptability and Learning Agility: Willingness to embrace new technologies, learn new skills, and adapt to defining and evolving project requirements. Ability to quickly grasp and apply new concepts and stay up-to-date with emerging trends in software engineering
Compensation packages at Scale for eligible roles include base salary,... $180000—$259000 USD (San Francisco/New York/Seattle) $162400—$233000 USD (Hawaii/Washington DC/Texas/Colorado) $135200—$194000 USD (St. Louis) PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role...
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Scale AI provides high-quality data and full-stack technologies that power the world’s leading models and enable enterprises and governments to build, deploy, and oversee AI applications that deliver real impact. They offer a data-centric, end-to-end solution to manage the entire machine learning lifecycle, combining cutting edge technology with operational excellence to help teams develop the highest-quality datasets.
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