Technical Sourcer (contract, hyrbrid, MH)

ROLE OVERVIEW

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.

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 RESPONSIBILITIES

Sourcing & Pipeline Development

• 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
• Execute passive sourcing strategies across academic networks, research publications, conference circuits (NeurIPS, ICLR, CVPR, MLSys, SC/Supercomputing, etc.), GitHub, and domain-specific professional communities
• Distinguish between meaningfully different research profiles and build separate, targeted pipelines for each role rather than treating them as interchangeable
• Develop deep market intelligence on competitive talent landscapes across both applied AI research and AI systems/infrastructure research domains
• Utilize advanced sourcing techniques including Boolean search, academic publication tracking, and community mapping to surface qualified passive candidates

Candidate Evaluation & Qualification

• 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
• Calibrate quickly with hiring managers across multiple research disciplines and adjust sourcing strategy per role
• Maintain high standards for candidate quality over volume, with emphasis on research output, publication record, systems contributions, and applied engineering experience
• Provide detailed, substantive candidate assessments that speak to research fit, not just resume summary

Pipeline Management & Communication

• Manage concurrent requisitions across both applied research and systems research verticals independently and efficiently
• Track pipeline metrics per role and provide regular updates on sourcing progress, candidate availability, and market conditions
• Communicate proactively with hiring managers on pipeline health and competitive dynamics specific to each talent segment
• Respond to feedback promptly and adjust sourcing approaches to continuously improve candidate quality

Stakeholder Collaboration

• Partner closely with recruiters and research hiring managers across disciplines to ensure seamless handoff of qualified candidates
• Build and maintain relationships with passive research and systems candidates for near and long-term pipeline development
• Collaborate with hiring managers to refine role profiles and candidate assessment criteria as the search evolves
• Support the broader recruiting function with market insights on AI research and AI systems talent trends

WORK LOGISTICS

• This role is onsite Monday, Wednesday, and Friday each week at the client's Palo Alto office
• Candidates must confirm commutability and schedule alignment before advancing in the interview process