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HomeJobsML Engineer Intern at Unsiloed-AI – Remote International Internship for Multimodal Models, Transformers and Applied Machine Learning
ML Engineer Intern at Unsiloed-AI – Remote International Internship for Multimodal Models, Transformers and Applied Machine Learning

ML Engineer Intern at Unsiloed-AI – Remote International Internship for Multimodal Models, Transformers and Applied Machine Learning

Sourced from app.dover.com

ML Intern

Remote
Full-Time
Posted March 21, 2026
Salary
1.5 lakh/Month
Batches
2026,2027
Apply Route
100% Direct

About The Role

Unsiloed-AI is an AI-focused technology company building intelligent systems that extract, interpret, and process complex information from unstructured data using advanced machine learning methods. The company works on modern AI challenges involving multimodal learning, transformer architectures, and production-grade model deployment. With internship opportunities available remotely for international candidates and in-office at San Francisco, the company offers early-career engineers direct exposure to applied research and product-oriented AI development. Interns work closely on model experimentation, dataset engineering, and performance benchmarking, making this role highly valuable for candidates aiming to strengthen both research depth and production ML understanding 🤖📊.

Responsibilities

Study academic research papers and identify state-of-the-art machine learning methods relevant to product goals.
Build, train, and fine-tune multimodal machine learning models for unstructured data extraction.
Curate and manage custom datasets for training, validation, and benchmarking.
Experiment with advanced deep learning architectures to improve model accuracy and efficiency.
Benchmark model performance across datasets using robustness and efficiency metrics.
Support deployment-related workflows for machine learning systems.

Requirements

  • Currently pursuing or recently completed a degree in Computer Science, Data Science, or related field.
  • Demonstrated experience in AI or ML projects, research, or academic publications.
  • Strong Python programming skills.
  • Familiarity with TensorFlow or PyTorch frameworks.
  • Understanding of machine learning deployment pipelines.
  • Exposure to containerized model deployment practices.

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