Internship - Machine Learning Research Engineer

Perplexity; United States; Remote; USD 34,440,000 - 48,216,000; Full time

Job summary

Perplexity is seeking a Machine Learning Research Engineer Intern to optimize deep learning models for search quality. You will train large-scale models using PyTorch and apply distributed training techniques to improve retrieval-based systems.

The role emphasizes research in representation learning and building retrieval and ranking models, including retrieval-augmented generation (RAG) pipelines. This internship offers hands-on experience in cutting-edge search technology.

Qualifications

• Solid foundation in PyTorch and an interest in representation learning.
• Experience with training large-scale deep learning models is a plus.
• Familiarity with retrieval and ranking concepts is beneficial.

Responsibilities

• Optimize deep learning models to improve search quality.
• Train large-scale models using PyTorch and distributed training techniques.
• Contribute to training and evaluating retrieval and ranking models, including RAG pipelines.

Skills

Deep learning

Representation learning

Distributed training

Tools

PyTorch

Job description

Perplexity is seeking a Machine Learning Research Engineer Intern to optimize deep learning models for search quality. This role is ideal for those with a solid foundation in PyTorch and an interest in representation learning.

As a Machine Learning Research Engineer Intern at Perplexity, you will focus on enhancing search quality through innovative research and model optimization. Your work will involve training large-scale deep learning models using PyTorch and leveraging distributed training techniques.

The internship emphasizes research in representation learning and the development of retrieval and ranking models. You will also build and optimize retrieval-augmented generation (RAG) pipelines, contributing to advancements in search and retrieval systems.