How much experience is required?

3–5 years of relevant experience for this Member of Technical Staff (Machine Learning Research Engineer) role.

What's the tech stack?

Joblaze extracted these technologies from the posting: Deep Learning, Machine Learning, PyTorch, Recommender Systems, Search, representation learning.

What seniority level is this role?

Perplexity AI targets staff-level candidates for this position.

Member of Technical Staff (Machine Learning Research Engineer) at Perplexity AI

Joblaze summary

In this role, the Machine Learning Research Engineer focuses on enhancing search technologies by developing and optimizing retrieval and ranking models. Key skills include proficiency in PyTorch and experience with distributed training, alongside a solid background in search systems and representation learning. This position is ideal for someone with at least three years of relevant experience and a strong publication record in AI/ML. The team collaborates closely across various departments to ensure efficient delivery of high-quality search solutions.

Joblaze insights

Listed 2 days ago — first seen on Joblaze September 25, 2026. Last confirmed on Perplexity AI's careers page September 27, 2026.

Quick facts

How much experience is required?: 3–5 years of relevant experience for this Member of Technical Staff (Machine Learning Research Engineer) role.
What's the tech stack?: Joblaze extracted these technologies from the posting: Deep Learning, Machine Learning, PyTorch, Recommender Systems, Search, representation learning.
What seniority level is this role?: Perplexity AI targets staff-level candidates for this position.
Is this full-time or contract?: Full-time for this Member of Technical Staff (Machine Learning Research Engineer) role at Perplexity AI.

From the original posting

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

Member of Technical Staff (Machine Learning Research Engineer)

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