Intern - AI Software Developer

Company Description

WD is building the infrastructure behind the AI-driven data economy. As AI scales, so does data. Every interaction, every model, every system generates data that must be stored, managed, and made accessible over time. That’s where we come in. We combine deep engineering expertise with global-scale manufacturing to deliver the storage systems that make AI possible, powering hyperscale data centers, cloud platforms, and enterprise infrastructure worldwide. This isn’t theoretical work. It’s real systems, at real scale, people solving some of the hardest challenges in technology today. We’re looking for people who want to build, solve, and operate at that level. Join us and let’s shape the future of data.

Job Description

ESSENTIAL DUTIES AND RESPONSIBILITIES:

• Assist in designing and developing AI models - focus on pattern recognition and GenAI feature enhancement.
• Collaborate with data scientists, engineers and IT to integrate AI technologies into our systems.
• Conduct data analysis and ensure the quality of data used in machine learning models.
• Participate in code reviews and maintain standard coding practices.
• Help in researching new AI trends and techniques that could improve our current systems.

Qualifications

REQUIRED:

• Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field
• Basic understanding of machine learning algorithms and principles
• Candidate is expected to work on site
• Basic familiarity with workflow automation / integration platforms

PREFERRED:

• A keen interest in artificial intelligence.
• Hands-on exposure to low-code workflow automation tools (eg.n8n) for connecting apps/APIs and building automated workflows
• Excellent problem-solving skills and ability to work in a team environment.
• Strong communication skills - Proficient in written and spoken English.

SKILLS:

• Strong programming skills in Python.
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., Scikit-learn, Pandas).