Full-Stack Engineer

Intuitive•Sunnyvale, CA

9h•$47 - $75•Hybrid

About The Position

Intuitive is hiring an entry-level Full-Stack Engineer to help develop a new internal AI-powered platform. The role involves building and maintaining frontend and backend features using a Python backend and React/TypeScript frontend, as well as integrating cloud services, AI APIs, and internal data sources. The engineer will help incorporate generative AI and large language model capabilities to help business teams find information, answer questions, and automate repetitive tasks. Working with senior engineers, product stakeholders, and business teams, the engineer will gather requirements, design and deliver features, communicate progress and tradeoffs, and incorporate user feedback. The position emphasizes secure handling of sensitive business data, data governance, testing, documentation, code reviews, CI/CD, and production observability. It is designed to build toward senior-level independence through mentorship and meaningful production work. The role is listed as a day-shift position with a set schedule and weekly onsite work; the percentage of onsite work will be defined by the leader. Compensation is listed by region: $56–$75 per hour in Region 1 and $47–$64 per hour in Region 2. Actual compensation depends on factors including experience, skills, qualifications, and job level.

Requirements

Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field.
Proficiency in Python through coursework, projects, or internships.
Exposure to React and TypeScript, such as building UI components or completing personal or class projects.
Familiarity with REST APIs and web application architecture.
Introductory experience with a major cloud provider: Azure, AWS, or GCP.
Basic understanding of Docker and deployment concepts.
Exposure to CI/CD concepts and tools such as GitHub Actions or GitLab CI.
Awareness of LLM APIs such as OpenAI or Azure OpenAI.
Conceptual understanding of authentication and access control, and awareness of data privacy and secure coding practices.
Ability to collaborate with non-technical stakeholders and translate business needs into technical requirements with guidance.
Comfort asking questions and iterating without perfect specifications.
Ability to take ownership of tasks from design through testing and deployment.
Writes clean, readable code and values testing, documentation, and code reviews.
Interest in working on production-grade software.
Entry-level / new graduate profile with 0–2 years of experience.

Nice To Haves

Curiosity about agentic patterns, tool-calling, and AI-powered workflows.
Coursework in Data Structures, Algorithms, Web Development, Cloud Computing, Databases, Software Engineering, or AI/ML.
OAuth 2.0 or OIDC familiarity.

Responsibilities

Help design, build, and maintain the frontend and backend of an internal AI-powered platform.
Build and maintain integrations with cloud services, AI APIs, and internal data sources.
Write clean, tested, and documented code across a Python backend and React/TypeScript frontend.
Integrate generative AI and LLM capabilities to help business teams find information, answer questions, and automate repetitive tasks.
Learn and apply agentic and tool-calling patterns with guidance from senior engineers.
Partner with business teams to understand workflows and translate them into software features.
Participate in requirements gathering, design reviews, and sprint planning with senior engineers and product stakeholders.
Communicate progress, blockers, and tradeoffs to technical and non-technical audiences.
Implement and follow secure authentication and access control practices appropriate for sensitive business data.
Apply data governance principles to handle information responsibly and compliantly.
Contribute to team practices including unit and integration testing, code reviews, and documentation.
Work within CI/CD pipelines to build, test, and deploy features to production.
Use logging, monitoring, and alerting tools to understand platform performance.
Gather and act on real user feedback to improve features.
Ship features incrementally while maintaining production quality.