Full-Stack Software Engineer

Intuitive•Sunnyvale, CA

9h•$47 - $75•Onsite

About The Position

Intuitive is hiring a junior/early-career Full-Stack Software Engineer to develop a new internal AI-powered platform. The engineer will build and maintain frontend and backend features, integrations with cloud services, AI APIs, and internal data sources, and support features from development through production deployment. The technology stack includes a Python backend and a React/TypeScript frontend. The role involves integrating generative AI and LLM capabilities to help business teams find information, answer questions, and automate workflows. The engineer will work with business stakeholders to understand workflows, translate needs into software, and iterate based on user feedback. Senior engineers provide guidance and mentorship on scoping, agentic patterns, system design, and architectural thinking, while the engineer takes end-to-end ownership of features and grows toward greater independence. The position also contributes to secure authentication and access control, responsible data governance, testing, code reviews, documentation, CI/CD deployment, and production observability. The job is onsite weekly, with the percentage of onsite work defined by the leader, and has a day shift. Target compensation varies by region: $56–$75 per hour in Region 1 and $47–$64 per hour in Region 2.

Requirements

1–2 years of professional experience; the role is described as junior/early-career.
Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field, or equivalent practical experience in lieu of a degree.
1–2 years of hands-on Python experience, professional or project-based.
Experience with React and TypeScript, including shipping or contributing to UI features in a real or production-like environment.
Solid understanding of REST API design and client-server architecture.
Working experience with at least one major cloud provider, with deployment or management of cloud resources in a real project.
Hands-on experience with Docker and containerized deployments.
Active experience working within CI/CD pipelines such as GitHub Actions, Azure DevOps, or similar.
Experience using LLM APIs such as OpenAI or Azure OpenAI in at least one project, whether professional, personal, or academic.
Working knowledge of OAuth 2.0/OIDC authentication flows, including implementation or debugging in a real application.
Awareness of secure coding practices and data privacy principles.
Ability to work with non-technical stakeholders to clarify requirements and translate feedback into actionable tasks.
Ability to take end-to-end ownership of features from ticket to production.
Comfort iterating amid ambiguity, making reasonable decisions, and knowing when to escalate.
Values code quality, testing, and documentation.
Proactively seeks feedback and applies it.

Nice To Haves

Exposure to FastAPI or a comparable asynchronous framework.
Experience with Azure; AWS or GCP experience is also considered.
Familiarity with agentic patterns and tool-calling.
Relevant coursework or self-study in web development, cloud computing, databases, software engineering, or AI/ML.

Responsibilities

Design, build, and maintain the frontend and backend of an internal AI-powered platform, owning features from development through production deployment.
Build and maintain integrations with cloud services, AI APIs, and internal data sources.
Write clean, tested, and documented code using a Python backend and React/TypeScript frontend.
Contribute to architectural discussions and develop system-design skills with guidance from senior engineers.
Integrate generative AI and LLM capabilities to help business teams find information, answer questions, and automate repetitive workflows.
Apply agentic and tool-calling patterns with support from senior engineers.
Partner with business teams to understand workflows and translate them into working software features.
Participate in design reviews, sprint planning, and retrospectives.
Communicate progress, blockers, and tradeoffs to technical and non-technical stakeholders.
Implement secure authentication and access control appropriate for sensitive business data.
Follow and contribute to data governance practices.
Contribute to and follow engineering practices including unit and integration testing, code reviews, and documentation.
Use CI/CD pipelines to build, test, and deploy features to production.
Use observability and monitoring tools to diagnose issues and understand production platform health.
Gather and act on user feedback to improve features over time.
Ship incrementally while maintaining production quality and balancing speed with reliability.