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Internship - Machine Learning Research Engineer

at Perplexity AI

Back to all Data Science / AI / ML jobs
Perplexity AI logo
Industry not specified

Internship - Machine Learning Research Engineer

at Perplexity AI

InternshipNo visa sponsorshipData Science/AI/ML

Posted 11 hours ago

No clicks

Compensation
Not specified

Currency: Not specified

City
Berlin
Country
Germany

Join the Internship Program in Berlin for 12–24 weeks full-time, in-person. You will push search quality forward by developing models, tooling, and data-driven improvements; train and optimize large-scale deep learning models with PyTorch in distributed settings, focusing on retrieval and ranking. You will conduct research in representation learning for search and retrieval, and build RAG pipelines for grounding and answer generation. Strong candidates will have an understanding of search systems, PyTorch proficiency, and interest in representation learning and evaluation.

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).

Internship - Machine Learning Research Engineer

at Perplexity AI

Back to all Data Science / AI / ML jobs
Perplexity AI logo
Industry not specified

Internship - Machine Learning Research Engineer

at Perplexity AI

InternshipNo visa sponsorshipData Science/AI/ML

Posted 11 hours ago

No clicks

Compensation
Not specified

Currency: Not specified

City
Berlin
Country
Germany

Join the Internship Program in Berlin for 12–24 weeks full-time, in-person. You will push search quality forward by developing models, tooling, and data-driven improvements; train and optimize large-scale deep learning models with PyTorch in distributed settings, focusing on retrieval and ranking. You will conduct research in representation learning for search and retrieval, and build RAG pipelines for grounding and answer generation. Strong candidates will have an understanding of search systems, PyTorch proficiency, and interest in representation learning and evaluation.

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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