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SEARCH SYSTEMS ENGINEER - FULLY REMOTE | UPTO $150/HR

mercor
Part-timemid€80-150/hour

Job description

About the job Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark , General Catalyst , Peter Thiel , Adam D'Angelo , Larry Summers , and Jack Dorsey . Position: Software Engineer — Agentic Search Systems Type: Contract Compensation: $80–$150/hour Location: Remote Role Responsibilities • Evaluate the accuracy and depth of AI-generated content in search systems to strengthen reasoning and rigor in model outputs . • Develop and optimize systems related to agentic search for improved performance and reliability. • Scale data infrastructure to support modern search systems and enhance retrieval capabilities. • Collaborate with AI research teams to quantify impact and improvements in search systems. • Work independently and asynchronously to meet deadlines while improving AI model performance . Qualifications Must-Have • Experience shipping production search systems. • Ownership of relevance or retrieval on systems used by real users. • Ability to quantify impact and improvements in search systems. • Experience building systems related to agentic search . • Experience scaling data infrastructure for search systems. Interview Process • 25 mins conversational interview. No coding, no take-home. • Follow-up with a paid 30-minute live conversation if interview stands out; $200 for your time. Application Process (Takes 20–30 mins to complete) • Upload resume • AI interview based on your resume • Submit form Resources & Support • For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome • For any help or support, reach out to: support@mercor.com PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.

Skills

AI-generated contentagentic searchdata infrastructuresearch systemsrelevanceretrievalmodel performancesystem optimization