Senior AI Search Engineer — Agentic Systems

Socket.dev; San Francisco (CA); On-site; USD 180,000 - 200,000; Full time

Benefits offered by this job

Health coverage

Parental leave

Family forming support

Paid time off

Wellbeing stipends

Mental health support

Commuter benefits

401(k) with employer match

Team events

Job summary

Ironclad seeks an experienced engineer to advance its agentic search and content understanding capabilities at enterprise scale. You will design and optimize architectures that blend LLMs with retrieval, while running robust benchmarks to guide improvements and ensure high-quality search results across complex contracts.

Ideal candidates have 4+ years building production search systems, with hands-on work in retrieval, LLM integration, and eval-driven workflows.

Qualifications

• 4 years experience building production search systems at scale.
• Expertise in learned/hybrid retrieval or production LLM agent systems.
• Experience with search frameworks and relevance tuning.
• Fluency with modern LLM APIs and multi-provider orchestration.
• Experience building eval-driven workflows and offline benchmarks.
• Strong ownership and communication.
• Care about scalable, reliable system design.
• Adaptable to fast-paced, outcome-driven environment.

Responsibilities

• Agentic search systems: evolve architecture for LLM+retrieval.
• Eval-driven development: run benchmarks and experiments.
• Search quality: contribute to quality bar.
• Content understanding & ingestion: turn raw documents into processed data for retrieval systems.

Skills

Search systems

Information retrieval

Content understanding

Recommendation systems

LLM APIs

Multi-provider orchestration

Eval-driven workflow

Ownership & communication

Tools

Elasticsearch

Solr

Vespa

OpenSearch

Embedding search

Job description

Ironclad seeks an experienced engineer to advance its agentic search and content understanding capabilities at enterprise scale. You will design and optimize architectures that blend LLMs with retrieval, while running robust benchmarks to guide improvements and ensure high-quality search results across complex contracts.

Ideal candidates have 4+ years building production search systems, with hands-on work in retrieval, LLM integration, and eval-driven workflows.