About the role

Aplyr's Quick Take

This role is for a Machine Learning Research Engineer focused on enhancing search technologies, particularly in retrieval and ranking. You'll be developing and optimizing deep learning models, deploying them, and collaborating with various teams to improve search quality. It's primarily an individual contributor position requiring advanced technical skills.

Good fit

Ideal candidates have 3-5 years of experience in search or recommender systems and a strong background in machine learning, particularly with PyTorch. A self-driven work style and a solid publication record in relevant AI/ML conferences are essential for success here.

Worth noting

The role emphasizes a strong research background, which may not appeal to those looking for purely applied engineering work. The focus on collaboration across teams suggests a dynamic work environment, but the spec lacks detail on day-to-day tasks.

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

Skills & Tags

go machine learning ai data product design

Aplyr's read

Perplexity AI revolutionizes search technology by delivering precise answers through advanced AI, attracting talent passionate about cutting-edge natural language processing.
Synthesized from recent postings & public sources

What's promising

•Innovative AI-driven search engine offering direct answers enhances user experience.
•Strong focus on advanced natural language processing sets it apart in the search sector.
•Diverse roles in AI and engineering indicate robust growth and innovation.

What to watch

•Highly competitive AI field may challenge sustained differentiation.
•Rapid growth could strain company culture and employee integration.
•Dependence on AI advancements may pose risks if technology stalls.

Why Perplexity AI

•Direct answer delivery system differentiates it from traditional search engines.
•Emphasis on natural language processing for precise query responses.
•Recent hires reflect a strategic focus on AI infrastructure and model training.

Aplyr’s read is generated by AI from public sources. Was it useful?

About Perplexity AI

Perplexity AI

perplexity.ai

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Perplexity AI is an AI-driven search engine that provides users with direct answers to their queries, leveraging advanced natural language processing technologies.