Software Engineering Intern, Product & Developer Productivity (Summer 2027)

Location: San Francisco, CA

Department: Internships

About the Role

HP IQ's Product Integrity & Developer Productivity team is responsible for building next-generation tools and systems that improve developer productivity and product quality at scale. We are specifically investing in agentic systems that automate complex workflows, from intelligent JIRA triage and root-cause analysis to test planning and quality automation. In this internship role, you will work directly with our hiring manager and team to design, build, and deploy agents that utilize pre-trained machine learning models to solve real problems for developers and quality engineers. This is an exceptional opportunity to be part of a team navigating the fundamental shift toward agentic development and to contribute meaningfully to tools that thousands of developers will rely on.

What You Might Do

As an intern on our team, you'll contribute to real-world engineering projects that explore how AI can improve the way software is built, tested, and maintained. Depending on business priorities and your team's needs, you may have the opportunity to:

• Design, build, and iterate on AI-powered tools and workflows that improve engineering productivity.
• Develop intelligent systems that help automate software development, testing, debugging, or quality assurance processes.
• Prototype and evaluate new approaches for applying large language models (LLMs) and agentic AI to solve complex engineering challenges.
• Collaborate with software engineers, machine learning engineers, product managers, designers, and other cross-functional partners to deliver impactful solutions.
• Participate in technical design discussions, code reviews, and collaborative problem-solving sessions.
• Analyze technical challenges, propose innovative solutions, and contribute fresh ideas that improve products, tools, and engineering workflows.
• Build, test, and iterate on production-quality software while learning modern engineering best practices.
• Present your work, share technical learnings, and contribute to a collaborative engineering culture.

Essential Qualifications 

• Currently pursuing or recently completed a bachelor's or graduate degree in Computer Science, Engineering, or a related field.
• Solid understanding of coding fundamentals and comfort with at least one programming language; you should be able to learn new languages and frameworks as needed.
• Familiarity with agentic systems, AI tools (such as Claude Code or OpenAI), or model utilization in applications; deep machine learning expertise is not required as we focus on using pre-trained models, not building them.
• Ability to break down complex problems into manageable steps and solve them independently while knowing when to ask for help.
• Willingness to question established processes and propose alternative approaches; comfort in an environment that values fresh thinking and continuous improvement.
• Strong communication skills; ability to explain your reasoning clearly and articulate the logic behind your technical choices.
• Familiarity with software development workflows, quality assurance, testing practices, or developer tooling (helpful but not required).

Preferred Skills

• Experience building or working with automation tools or systems that improve workflow efficiency.
• Exposure to or familiarity with JIRA, continuous integration/continuous deployment (CI/CD) systems, or testing frameworks.
• Experience working in cross-functional teams or in environments where you collaborated with people from different technical backgrounds.
• Understanding or coursework in machine learning, AI, or agent systems.
• Example projects demonstrating agentic thinking, such as personal projects using AI tools to automate tasks.

Hourly Pay Range: $45.00–$51.00 per hour. Actual compensation will be determined based on factors such as skills, qualifications, and experience.

2027 Internship Program Dates: May/June–August/September 2027. Final dates will be based on student schedules.