Remote Lead Applied Scientist - Search & IR

Ladders; United States; Remote; USD 148,000 - 274,000; Full time

Benefits offered by this job

Flexibility

Career development

Flexible vacation

Mental health days

Inclusive culture

Volunteer days

ESG initiatives

Impact on justice & truth

Job summary

Ladders is seeking a Lead Applied Scientist, Search & Information Retrieval to shape search and retrieval across large-scale content experiences. The role emphasizes ranking quality, relevance, and system performance, with collaboration across engineering teams to design scalable architectures.

The position blends advanced applied science with hands-on production work, including retrieval pipelines, evaluation design, and modern ML techniques. Remote in the US, visa sponsorship not available.

Qualifications

• PhD in CS, IR, AI, ML or NLP preferred.
• 8+ years in production search/IR/ranking/recommendation systems.
• Publications at SIGIR/ACL/EMNLP/NeurIPS/ICLR/KDD/WWW preferred.
• Strong production Python skills with PyTorch & Hugging Face Transformers.
• Expertise in search engine architecture, indexing & ingestion pipelines.

Responsibilities

• Design and deploy search architectures for large-scale content and enterprise information.
• Build and optimize ingestion pipelines for document retrieval.
• Develop ranking and re-ranking systems using traditional and modern techniques.
• Enhance retrieval quality through semantic and hybrid retrieval methods.
• Create evaluation frameworks for measuring retrieval performance and user outcomes.
• Lead technical decisions on indexing strategies and search infrastructure.
• Collaborate with engineering teams to deliver scalable search services.

Skills

Python

PyTorch

Hugging Face Transformers

Search engine architecture

Indexing systems

Ingestion pipelines

Ranking systems

Education

PhD in Computer Science / Information Retrieval / AI / ML / NLP

Job description

Ladders is seeking a Lead Applied Scientist, Search & Information Retrieval to shape search and retrieval across large-scale content experiences. The role emphasizes ranking quality, relevance, and system performance, with collaboration across engineering teams to design scalable architectures.

The position blends advanced applied science with hands-on production work, including retrieval pipelines, evaluation design, and modern ML techniques. Remote in the US, visa sponsorship not available.