Senior Full Stack Engineer - USDS

About the Team

The Cyber Defense & Engineering team is missioned to run and operate security infrastructures, platforms and technologies, as well as to support cross-functional teams to protect our users, products and infrastructures. In this team you'll have a unique opportunity to have first-hand exposure to the strategy of the company in key security initiatives, especially in deploying and maintaining scalable and secure-by-design systems and solutions. Our challenges are not your regular day-to-day technical problems; you'll be part of a team that's developing new solutions to new challenges of a kind not previously addressed by big tech. It's working fast, at scale, and we're making a difference.



On-site presence across teams allows the company to operate with greater speed, alignment, and agility — especially in areas like real-time decision-making, team development, and integrated execution. As such, the company is shifting from a hybrid work model to a fully in-person schedule up to 5 days a week.



About the Role

You are a Senior Engineer first, and an AI-builder second. While your output will be autonomous AI agents, the day-to-day responsibilities include building the robust, scalable, and secure distributed systems that allow those agents to operate. This role will join a "Tiger Team". You will own the full vertical—from the React frontend where humans supervise agents, to the Python/Go backend services that power them, down to the Kubernetes infrastructure they run on.



Responsibilities

To succeed in this role, you are expected demonstrate mastery of the following engineering fundamentals:

1. Backend Engineering & System Architecture

- Language Proficiency: Expert-level fluency in Python (FastAPI, Django, or Flask) is mandatory for AI orchestration.

- API Design: Extensive experience designing RESTful APIs and GraphQL schemas. You understand idempotency, rate limiting, authentication (OAuth2/JWT), and API versioning strategies.

- Distributed Systems: Deep understanding of microservices architecture, message queues (Kafka, RabbitMQ, or SQS), and asynchronous task processing (Celery, Temporal). You know how to handle race conditions and ensure data consistency across services.

- Database Design:

- Relational: Expert SQL skills (PostgreSQL/MySQL). You can design normalized schemas, optimize complex queries, and understand indexing strategies for performance.

- NoSQL: Familiarity with key-value stores (Redis for caching/session management) and document stores (MongoDB/DynamoDB) for flexible agent memory.



2. Frontend Engineering

- Modern Frameworks: Deep expertise in React (TypeScript) ecosystems. You are comfortable using Next.js for server-side rendering and static site generation.

- State Management: Proficiency with complex state management libraries (Redux Toolkit, Zustand, or React Query) to handle real-time agent status updates without UI lag.

- UI/UX Implementation: Ability to implement responsive, accessible designs using modern styling systems (Tailwind CSS, Styled Components, or Material UI). You aren't just "patching" UIs; you build intuitive dashboards for non-technical operators.



3. Infrastructure & DevOps

- Containerization: You live in the terminal. Experience writing multi-stage Dockerfiles and managing container orchestration with Kubernetes (Helm charts, kubectl).

- CI/CD Pipelines: Experience setting up comprehensive CI/CD pipelines (GitHub Actions, GitLab CI) that include automated linting (flake8/black/ESLint), unit testing (PyTest/Jest), and integration testing before deployment.

- Observability: You build systems that are easy to debug. Experience integrating observability tools (Prometheus, Grafana, Datadog, Sentry) to trace requests and monitor system health.



The "AI-Native" Layer

While the skills above are the foundation, the following are the "force multipliers" required for this specific team:

- Vector Infrastructure: Experience running vector databases (Pinecone, Weaviate, Milvus) in production, including hybrid search (keyword + semantic) strategies.

- Agent Orchestration: Practical experience with LangChain or LangGraph to manage agent state, memory, and tool execution loops.

- LLM Integration: Deep familiarity with OpenAI/Anthropic APIs, specifically Function Calling (structured output) and context window management.