Technical Sourcer (contract, hyrbrid, MH)

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          "text": "<p><strong>ROLE OVERVIEW</strong></p><p>Our 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.</p><p>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.</p><p><strong>KEY RESPONSIBILITIES</strong></p><p><strong>Sourcing &amp; Pipeline Development</strong></p><ul><li>Build 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 Infrastructure</li><li>Execute passive sourcing strategies across academic networks, research publications, conference circuits (NeurIPS, ICLR, CVPR, MLSys, SC/Supercomputing, etc.), GitHub, and domain-specific professional communities</li><li>Distinguish between meaningfully different research profiles and build separate, targeted pipelines for each role rather than treating them as interchangeable</li><li>Develop deep market intelligence on competitive talent landscapes across both applied AI research and AI systems/infrastructure research domains</li><li>Utilize advanced sourcing techniques including Boolean search, academic publication tracking, and community mapping to surface qualified passive candidates</li></ul><p><strong>Candidate Evaluation &amp; Qualification</strong></p><ul><li>Screen 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 roles</li><li>Calibrate quickly with hiring managers across multiple research disciplines and adjust sourcing strategy per role</li><li>Maintain high standards for candidate quality over volume, with emphasis on research output, publication record, systems contributions, and applied engineering experience</li><li>Provide detailed, substantive candidate assessments that speak to research fit, not just resume summary</li></ul><p><strong>Pipeline Management &amp; Communication</strong></p><ul><li>Manage concurrent requisitions across both applied research and systems research verticals independently and efficiently</li><li>Track pipeline metrics per role and provide regular updates on sourcing progress, candidate availability, and market conditions</li><li>Communicate proactively with hiring managers on pipeline health and competitive dynamics specific to each talent segment</li><li>Respond to feedback promptly and adjust sourcing approaches to continuously improve candidate quality</li></ul><p><strong>Stakeholder Collaboration</strong></p><ul><li>Partner closely with recruiters and research hiring managers across disciplines to ensure seamless handoff of qualified candidates</li><li>Build and maintain relationships with passive research and systems candidates for near and long-term pipeline development</li><li>Collaborate with hiring managers to refine role profiles and candidate assessment criteria as the search evolves</li><li>Support the broader recruiting function with market insights on AI research and AI systems talent trends</li></ul><p><strong>WORK LOGISTICS</strong></p><ul><li>This role is onsite Monday, Wednesday, and Friday each week at the client's Palo Alto office</li><li>Candidates must confirm commutability and schedule alignment before advancing in the interview process</li></ul>",
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          "text": "<p><strong>QUALIFICATIONS</strong></p><p><strong>Required</strong></p><ul><li>3 or more years of sourcing or recruiting experience, with at least 1 year focused on AI, ML, deep tech, or AI systems talent</li><li>Demonstrated ability to source for Research Scientist, Research Engineer, or equivalent specialized roles across more than one technical domain</li><li>Familiarity with where research talent concentrates: arXiv, Semantic Scholar, NeurIPS/ICLR/CVPR/MLSys attendee lists, university labs, and open-source AI and systems communities</li><li>Ability to read a role brief and distinguish meaningfully between profiles: evaluation research vs. applied research vs. systems research vs. agent/interaction design</li><li>Strong written outreach skills with the ability to craft credible, compelling messages to PhD-level or research-track candidates across different specializations</li><li>Proficiency with LinkedIn Recruiter, GitHub, and at least one academic or research-specific sourcing channel</li><li>Ability to manage multiple concurrent requisitions across distinct technical disciplines independently, with minimal ramp time given the 6-month contract structure</li></ul><p><strong>Preferred</strong></p><ul><li>Degree from a research-oriented university (examples: UC Berkeley, Stanford, CMU, UIUC, Michigan) or equivalent demonstrated intellectual rigor</li><li>Background in or strong familiarity with generative AI, multimodal AI, LLM training infrastructure, or agent-focused product and research environments</li><li>Exposure to systems-level research hiring: performance optimization, distributed training, compiler stacks, or ML infrastructure roles</li><li>Comfort reading abstracts or project descriptions to assess topical relevance to a specific open role</li><li>Experience writing candidate assessments that speak to research fit, domain alignment, and role-specific competency signals</li><li>Demonstrated ambition and trajectory: promotions, scope expansion, or a history of taking on progressively harder roles over time</li><li>Experience with pipeline analytics and data-driven sourcing optimization</li></ul>",
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          "text": "<p>Employer.com is part of a family of incredible brands alongside Flawless Recruit and Recruiter.com. Together, we provide talent acquisition services to fit the unique hiring challenges of our clients.&#xa0;Whether they need help building recruiting processes, attracting top talent, or payrolling contractors, we can help.&#xa0;</p><p>&#xa0;</p>",
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