Technical Sourcer (contract, hyrbrid, MH)

ROLE OVERVIEWOur client is an AI lab headquartered in the SF Bay Area. The role is hybrid and a six-month contract. There is a strong possibility of extension for the right individual, or to turn into full-time, as long as you can come on-site at least three days a week.The team is approximately 160 employees as of 2026, lean, high-output, and scaling rapidly. This Sourcer will own research and applied engineering hiring across two distinct but related talent verticals: applied research (evaluation, controllability, agent behavior, personalization) and systems research (performance optimization, training infrastructure). The work directly impacts the company's AI research capability and long-term product roadmap.KEY RESPONSIBILITIESSourcing & Pipeline DevelopmentBuild and maintain quality pipelines across a specialized set of research and engineering roles, including Qualitative Evaluation Engineer, Research Engineer (Evaluations), Research Scientist/Engineer in Controllability and Personalization, Applied Research Scientist/Engineer, Agent Behavior Designer, Research Scientist/Engineer in Performance Optimization, and Research Scientist/Engineer in Training InfrastructureExecute passive sourcing strategies across academic networks, research publications, conference circuits (NeurIPS, ICLR, CVPR, MLSys, SC/Supercomputing, etc.), GitHub, and domain-specific professional communitiesDistinguish between meaningfully different research profiles and build separate, targeted pipelines for each role rather than treating them as interchangeableDevelop deep market intelligence on competitive talent landscapes across both applied AI research and AI systems/infrastructure research domainsUtilize advanced sourcing techniques including Boolean search, academic publication tracking, and community mapping to surface qualified passive candidatesCandidate Evaluation & QualificationScreen and qualify candidates with an understanding of the distinct competency profiles across the role set: evaluation methodology and qualitative research skills for evaluation roles; controllability, RLHF, and personalization research for scientist/engineer roles; distributed systems, compiler optimization, and training stack experience for systems roles; and interaction design and behavioral modeling for agent behavior rolesCalibrate quickly with hiring managers across multiple research disciplines and adjust sourcing strategy per roleMaintain high standards for candidate quality over volume, with emphasis on research output, publication record, systems contributions, and applied engineering experienceProvide detailed, substantive candidate assessments that speak to research fit, not just resume summaryPipeline Management & CommunicationManage concurrent requisitions across both applied research and systems research verticals independently and efficientlyTrack pipeline metrics per role and provide regular updates on sourcing progress, candidate availability, and market conditionsCommunicate proactively with hiring managers on pipeline health and competitive dynamics specific to each talent segmentRespond to feedback promptly and adjust sourcing approaches to continuously improve candidate qualityStakeholder CollaborationPartner closely with recruiters and research hiring managers across disciplines to ensure seamless handoff of qualified candidatesBuild and maintain relationships with passive research and systems candidates for near and long-term pipeline developmentCollaborate with hiring managers to refine role profiles and candidate assessment criteria as the search evolvesSupport the broader recruiting function with market insights on AI research and AI systems talent trendsWORK LOGISTICSThis role is onsite Monday, Wednesday, and Friday each week at the client's Palo Alto officeCandidates must confirm commutability and schedule alignment before advancing in the interview process