Are you passionate about advancing artificial intelligence and applying it to real-world problems? Join our Applied Science team and contribute to cutting-edge research in natural language processing (NLP), deep learning, and generative AI.
As an Applied Science Intern, you will work closely with experienced researchers and engineers to design, develop, and evaluate scalable machine learning models. This role offers hands-on experience with large language models (LLMs), transformers, and neural networks, along with the opportunity to contribute to impactful, production-level systems.
You will be involved in solving complex challenges such as model fine-tuning, named entity recognition (NER), recommendation systems, and question answering. The internship emphasizes both research and practical implementation, enabling you to bridge theory and real-world applications.
This is a highly collaborative role where you will partner with cross-functional teams to address business problems using advanced AI techniques. The ideal candidate is self-motivated, detail-oriented, and comfortable working in a fast-paced, evolving environment.
Interns benefit from access to large-scale computing resources, current research, and mentorship from experienced professionals, supporting both technical growth and professional development.
Key Responsibilities
- Develop and implement scalable machine learning models in areas such as LLMs and generative AI
- Fine-tune state-of-the-art models and design new algorithms
- Work on large-scale datasets to solve problems in recommendation systems, NLP, and question answering
- Collaborate with cross-functional teams to deliver practical AI solutions
- Present findings and contribute to knowledge sharing within the team
Basic Qualifications
- Currently enrolled in a PhD program
- Available for a full-time (40 hours/week) internship for the duration
- Willing to relocate to the internship location
- Proficiency in at least one programming language (e.g., Python, Java, C++)
Preferred Skills
- Experience with LLMs, NLP/NLU, or generative AI
- Familiarity with transformers, deep learning, and neural networks
- Knowledge of recommendation systems, NER, or question answering
- Strong foundation in statistics and machine learning