Technical Sourcer (contract, hyrbrid, MH)

Job Mobz•Redwood City, CA

1d•$70 - $90•Hybrid

About The Position

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.

Requirements

3 or more years of sourcing or recruiting experience, with at least 1 year focused on AI, ML, deep tech, or AI systems talent
Demonstrated ability to source for Research Scientist, Research Engineer, or equivalent specialized roles across more than one technical domain
Familiarity with where research talent concentrates: arXiv, Semantic Scholar, NeurIPS/ICLR/CVPR/MLSys attendee lists, university labs, and open-source AI and systems communities
Ability to read a role brief and distinguish meaningfully between profiles: evaluation research vs. applied research vs. systems research vs. agent/interaction design
Strong written outreach skills with the ability to craft credible, compelling messages to PhD-level or research-track candidates across different specializations
Proficiency with LinkedIn Recruiter, GitHub, and at least one academic or research-specific sourcing channel
Ability to manage multiple concurrent requisitions across distinct technical disciplines independently, with minimal ramp time given the 6-month contract structure

Nice To Haves

Degree from a research-oriented university (examples: UC Berkeley, Stanford, CMU, UIUC, Michigan) or equivalent demonstrated intellectual rigor
Background in or strong familiarity with generative AI, multimodal AI, LLM training infrastructure, or agent-focused product and research environments
Exposure to systems-level research hiring: performance optimization, distributed training, compiler stacks, or ML infrastructure roles
Comfort reading abstracts or project descriptions to assess topical relevance to a specific open role
Experience writing candidate assessments that speak to research fit, domain alignment, and role-specific competency signals
Demonstrated ambition and trajectory: promotions, scope expansion, or a history of taking on progressively harder roles over time
Experience with pipeline analytics and data-driven sourcing optimization

Responsibilities

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
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
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
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