Member of Technical Staff (Machine Learning Research Engineer)

Perplexity; Berlin; Vor Ort; EUR 90.000 - 150.000; Vollzeit

Zusammenfassung

Perplexity in Berlin seeks an experienced Machine Learning Research Engineer to push the next generation of retrieval and ranking for advanced search technologies.

You will design and train large-scale models with PyTorch, work with distributed training techniques, and collaborate with Data, AI, Infrastructure, and Product teams to deliver high-quality, scalable solutions.

Qualifikationen

• 3+ years (preferably 5+) working on search, recommender systems, or related research areas.
• Experience with large-scale deep learning models and evaluation principles.
• Strong publication record in AI/ML conferences.

Aufgaben

• Push search quality forward through models, data, tools, or other leverage.
• Architect and build core components of the search platform and model stack.
• Design, train, and optimize large-scale deep learning models using PyTorch with distributed training and hardware acceleration.
• Conduct representation learning research including contrastive learning for multilingual and multimodal search.
• Deploy models in a scalable and performant way from boosting algorithms to LLMs.
• Build and optimize RAG pipelines for grounding and answer generation.
• Collaborate with Data, AI, Infrastructure, and Product teams to ensure fast, high-quality delivery.

Kenntnisse

Search & Retrieval

Recommender Systems

Model Optimization

Representation Learning

Multilingual Modeling

Multimodal Modeling

PyTorch

Distributed Training

Research Publication

Tools

PyTorch

DeepSpeed

FSDP

Distributed Training

RAG Pipelines

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.

Responsibilities

• 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

Qualifications

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