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Member of Technical Staff (Machine Learning Research Engineer)
Summary by AIEngineer.careers
Member of Technical Staff (Machine Learning Research Engineer) is a Machine Learning Research Engineer role (full-time). with Perplexity. in BERLIN, DE. Imported listing (source: feeny.ai). Apply on the employer's site (feeny.ai).
Imported listing (source: feeny.ai) · Apply on feeny.ai
BERLIN · DE
—
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from feeny.ai
Perplexity is hiring an experienced Machine Learning Research Engineer to advance next-generation search technologies, focusing on retrieval and ranking. You will design, train, and optimize large-scale deep learning models, build RAG pipelines, and conduct research in representation learning. This role collaborates closely with Data, AI, Infrastructure, and Product teams to deliver high-quality search experiences.
Responsibilities
- Push search quality forward through models, data, and tooling
- Architect and build core components of the search platform and model stack
- Design, train, and optimize large-scale deep learning models using PyTorch, distributed training (PyTorch Distributed, DeepSpeed, FSDP), and hardware acceleration
- Conduct advanced research in representation learning, including contrastive, multilingual, and multimodal modeling
- 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
Requirements
- Deep understanding of search and retrieval systems, including quality evaluation principles and metrics
- Proven track record with large-scale search or recommender systems
Perplexity
San Francisco · US · 1014+ employees
Perplexity is an AI-powered answer engine and search platform founded in 2022. It uses large language models to provide accurate, real-time, and well-sourced responses to user queries, aimed at transforming the search and information retrieval experience.
