Machine Learning Research Engineer (Member of Technical Staff)

Perplexity AI; Berlin; Vor Ort; EUR 90.000 - 140.000; Vollzeit

Zusammenfassung

Perplexity AI is seeking an experienced Machine Learning Research Engineer to advance the next generation of search technologies, focusing on retrieval and ranking. You will architect and build core components of the model stack and deploy models at scale to improve search quality.

The ideal candidate has 3+ years in search or recommender systems, deep expertise in PyTorch with distributed training, and a strong publication record in AI/ML venues.

Qualifikationen

• 3+ years in search, recommender systems, or related research.
• Strong proficiency with PyTorch and distributed training.
• Proven track record with large-scale search or recommender systems.
• Expertise in representation learning for multilingual and multimodal applications.
• Knowledge of quality evaluation principles and metrics.
• Publication record in AI/ML conferences is a plus.

Aufgaben

• Design, train, and optimize large-scale deep learning models for retrieval and ranking.
• Architect and build core components of the search platform and model stack.
• Deploy models at scale with efficient performance.
• Collaborate with Data, AI, Infrastructure, and Product teams to deliver high-quality results.

Kenntnisse

PyTorch

Distributed training

Search systems

Recommender systems

Representation learning

Multilingual & multimodal

Evaluation metrics

Publications

Tools

DeepSpeed

FSDP

Jobbeschreibung

• Perplexity is seeking an experienced Machine Learning Research Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking
• Relentlessly push search quality forward — through models, data, tools, or any other leverage available
• Architect and build core components of the search platform and model stack
• Design, 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 advanced research in representation learning, including contrastive learning, multilingual, and multimodal modeling for search and retrieval
• Deploy models — from boosting algorithms to LLMs — in a scalable and performant way
• Build and optimize RAG pipelines for grounding and answer generation
• Collaborate with Data, AI, Infrastructure, and Product teams to ensure fast and high-quality delivery

Minimum of 3 years (preferably 5+) working on search, recommender systems, or closely related research areasStrong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large modelsProven track record with large-scale search or recommender systemsExpertise in representation learning, including contrastive learning and embedding space alignment for multilingual and multimodal applicationsDeep understanding of search and retrieval systems, including quality evaluation principles and metricsSelf-driven, with a strong sense of ownership and executionStrong publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, SIGIR)