About this role
- Implement and test well-scoped features within the Eval platform — e.g., pieces of the virtualization harness, telemetry/cost dashboards, or friction-ticketing logic — under guidance from senior engineers.
- Help normalize capability-team use-case documentation into the common agent-readable format.
- Write unit and integration tests, fix bugs, and participate in code reviews and design discussions.
- Learn and apply prompting techniques for agent instructions in a supervised capacity, iterating on reliability and cost.
- Support maintenance of the CI/CD pipeline that re-runs use cases on every deploy to catch regressions.
Responsibilities
- 2–4 years of professional software engineering experience (or equivalent internship experience plus strong fundamentals).
- Proficiency in Python or Java; familiarity with Spring Boot, GraphQL, or React is a plus.
- Strong CS fundamentals — data structures, algorithms, and a habit of writing tested, readable code.
- Curiosity about LLMs and AI agents, with willingness to learn prompting and agent-orchestration patterns on the job.
Qualifications
- Coursework, projects, or internship experience involving AI/ML or LLM APIs.
- Exposure to CI/CD pipelines, Docker, or basic virtualization concepts.
- Familiarity with GraphQL and React for building internal tooling UIs.
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits ). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.