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The world's fastest data platform.
The Role
The internship involves research and development in AI and NLP, focusing on semantic systems, embedding-based retrieval, and collaboration on publishable research.
Summary Generated by Built In
Who we are
We're Redis. We built the product that runs the fast apps our world runs on. (If you checked the weather, used your credit card, or looked at your flight status online today, you’re welcome.) At Redis, you’ll work with the fastest, simplest technology in the business—whether you’re building it, telling its story, or selling it to our 10,000+ worldwide customers. We’re creating a faster world with simpler experiences. You in?
Position Overview
We are seeking a highly motivated Masters or Ph.D. student for a 3 month paid internship focused on cutting-edge natural language processing and AI research. This is an excellent opportunity to work on advanced semantic systems and contribute to meaningful research in the field.Duration & Compensation
We are seeking a highly motivated Masters or Ph.D. student for a 3 month paid internship focused on cutting-edge natural language processing and AI research. This is an excellent opportunity to work on advanced semantic systems and contribute to meaningful research in the field.Duration & Compensation
- Duration: 3 months
- Type: Paid internship
- Start Date: Flexible
Required Qualifications
- Currently enrolled in a Masters or Ph.D. program in Computer Science, Machine Learning, NLP, or related field
- Strong background in Natural Language Processing (NLP)
- Hands-on experience in at least one of the following areas:
- Embedding-based retrieval systems
- Representation learning using deep learning
- Reliable LLM/RAG/Agentic systems
Bonus Points
- Experience with fine-tuning embedding models
- Published papers in NLP, representation learning, or related fields
- Familiarity with modern ML frameworks (PyTorch, TensorFlow, Hugging Face)
- Experience with vector databases and similarity search systems
What You'll Work On
- Design and implement advanced semantic caching systems
- Develop and optimize embedding-based retrieval mechanisms
- Research and experiment with state-of-the-art NLP models
- Contribute to the development of reliable AI systems
- Collaborate on research that may lead to publications
Please submit
- Resume/CV highlighting relevant experience and publications
- Links to any relevant projects, papers, or GitHub repositories
As a global company, we value a culture of curiosity, diversity of thought, and innovation from our employees, customers, and partners. Redis is committed to a diverse and inclusive work environment where all employees’ differences are celebrated and supported, and everyone feels safe to bring their authentic selves to work. Redis is dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national orientation, sexual orientation, age, marital status, disability, gender identity, gender expression, Veteran status, or any other classification protected by federal, state, or local law. We strive to create a workplace where every voice is heard, and every idea is respected.
Redis is committed to working with and providing access and reasonable accommodation to applicants with mental and/or physical disabilities. If you think you may require accommodations for any part of the recruitment process, please send a request to [email protected]. All requests for accommodations are treated discreetly and confidentially, as practical and permitted by law.
Any offer of employment at Redis is contingent upon the successful completion of a background check, consistent with applicable laws.
Redis reserves the right to retain data longer than stated in the privacy policy in order to evaluate candidates.
Top Skills
Deep Learning
Embedding-Based Retrieval Systems
Hugging Face
Machine Learning
Natural Language Processing
PyTorch
TensorFlow
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The Company
What We Do
Redis is the world's fastest data platform. We provide cloud and on-prem solutions for caching, vector search, and more that seamlessly fit into any tech stack. With fast setup and fast support, we make it simple for digital customers to build, scale, and deploy the fast apps our world runs on.
