Centific
Technical Intern
Remote Work( USA) · Intern · Internship
Sponsorship not specifiedDetected 31 days ago
PythonVector DatabasesMachine LearningTensorFlowPyTorchNLPComputer VisionLLMsRAGLLMOpsMLOpsResearchCollaboration
About the role
- About Centific Centific is a frontier AI data foundry that curates diverse, high-quality data, using our purpose-built technology platforms to empower the Magnificent Seven and our enterprise clients with safe, scalable AI deployment.
- Our team includes more than 150 PhDs and data scientists, along with more than 4,000 AI practitioners and engineers.
- Our zero-distance innovation™ solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster.
Requirements
- Hands-on experience with cutting-edge AI ML industry projects
Skills
- Proficiency in Python
- Expertise in PyTorch and TensorFlow
- Expertise in one or more of RAG, Vector DBs, LLM Fine-tuning,
- Strong understanding of Transformer Architecture
- Familiarity with Flow-based models
Benefits
- Competitive stipend package
- Are you a PhD / MS student working in NLP, Computer Vision, LLMs, VLMs, Responsible AI and passionate about solving a real-world problem by pushing the boundaries of technology?
- Do you want to delve into the latest industry advancements in NLP, Computer Vision, Machine Learning, and more?
- Vision-Language Models (VLM)
- Reinforcement Learning
- Federated Learning / Differential Privacy
Company info
- Our mission is to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers.
- Join us in shaping the future of technology and make your research stand out with real-world applications.
Visa & Work Authorization
- pplicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, citizenship status, age, mental or physical disability, medical condition, sex (including pregnancy)
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This listing is sourced directly from Centific's careers page and normalized into a canonical job model.