Principle Machine Learning Architect (Enterprise Agentic Search)

Location: Remote, United States; Seattle listed as the location

Base salary: $196,461–$309,025 annually, depending on geographic pay zone. Additional benefits, bonuses, commissions, and equity may apply.

Company

hirify.global develops software products that help teams collaborate and manage different types of work.

What you will do

• Own the architecture and technical direction for enterprise agentic search across retrieval, query understanding, source discovery, tool use, agent orchestration, and context construction.
• Design systems for heterogeneous enterprise content, permissions, tenant isolation, freshness, domain-specific terminology, scalability, latency, cost, and reliability.
• Define benchmarks, datasets, evaluation environments, observability, and reproducible experiments for retrieval quality, evidence coverage, agent behavior, and task success.
• Set the technical direction for LLM training, including training data, supervised fine-tuning, preference optimization, reinforcement learning, evaluation, and serving.
• Build prototypes and reference implementations, investigate production failures, and use experiments to validate architectural trade-offs.
• Lead multi-quarter technical direction across search, agent, and ML platform teams through design reviews, mentorship, and implementation guidance.

Requirements

• Substantial hands-on experience architecting, delivering, and evolving production RAG or agentic search systems used by real customers, especially in enterprise applications.
• Experience owning architecture from customer needs through system design, implementation, launch, and evolution, with measurable improvements in search quality, task success, or customer outcomes.
• Deep understanding of LLM-based search and agent systems, including retrieval, tool use, multi-step search, context construction, grounded reasoning, and LLM adaptation and deployment.
• Strong evaluation, experimentation, software engineering, distributed-systems, data-pipeline, model-serving, and production-observability skills.
• Experience with enterprise knowledge and workflows involving heterogeneous data, access permissions, freshness, domain-specific terminology, and information spanning multiple systems.
• Ability to influence technical direction across teams, mentor senior engineers and scientists, and balance model quality, latency, cost, reliability, privacy, and access control.

Culture & Benefits

• Flexible work options combining remote work and office work.
• Health and wellbeing resources.
• Paid volunteer days and community engagement support.
• Additional benefits may include bonuses, commissions, and equity.
• Workplace accommodations and adjustments can be supported during the recruitment process.