Job Description
Internship Program Berlin
Internship program: 12 - 24 weeks, full-time, in-person in the Berlin office.
Responsibilities
Relentlessly push search quality forward — through models, data, tools, or any other leverage available.
Train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models.
Conduct research in representation learning, including contrastive learning, multilingual, evaluation, and multimodal modeling for search and retrieval.
Build and optimize RAG pipelines for grounding and answer generation.
Qualifications
Understanding of search and retrieval systems, including quality evaluation principles and metrics.
Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models.
Interested in representation learning, including contrastive learning, dense & sparse vector representations, representation fusion, cross-lingual representation alignment, training data optimization and robust evaluation.
Publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR).
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How to Apply
About Perplexity
Perplexity
Berlin
Frequently Asked Questions
Where is the Internship - Machine Learning Research Engineer position at Perplexity located?
The Internship - Machine Learning Research Engineer role at Perplexity is based in Berlin.
What type of employment is the Internship - Machine Learning Research Engineer role at Perplexity?
This Internship - Machine Learning Research Engineer position is offered as Full Time.
What skills are needed for the Internship - Machine Learning Research Engineer role at Perplexity?
Key skills and focus areas for this role include Search.
How do I apply for the Internship - Machine Learning Research Engineer position at Perplexity?
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