Technical Sourcer (contract, hyrbrid, MH)

Employer.com•Redwood City, CA

3h•Hybrid

About The Position

Our client is a frontier generative 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 sit at the top of the funnel for Applied AI, Research, and Distributed Engineering hiring. The work is high-bar and low-volume by design: the goal is qualified, assessed candidates delivered to a full-cycle recruiter, not raw pipeline volume. The work directly impacts the company's core AI research engine and long-term technical moat.

Requirements

3 or more years of sourcing or recruiting experience, with at least 1 year focused on AI, ML, research, or distributed systems talent
Demonstrated ability to source Research Scientists, Research Engineers, or equivalent specialized roles, not just software engineers or GTM profiles
Familiarity with where research talent concentrates: arXiv, Semantic Scholar, NeurIPS/ICLR/CVPR/MLSys attendee lists, university labs, and open-source AI communities
Ability to distinguish meaningfully between research 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
Proficiency with LinkedIn Recruiter, GitHub, and at least one academic or research-specific sourcing channel
Ability to operate independently across multiple concurrent sourcing workstreams with minimal ramp time

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 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 targeted pipelines for specialized Applied AI and Research engineering roles, including Applied Research Scientists, Research Engineers, Research Scientists in Controllability and Personalization, Qualitative Evaluation Engineers, Research Engineers in Evaluations, and Research Scientists in Performance Optimization and Training Infrastructure
Execute passive sourcing strategies across academic networks, research publications, conference circuits (NeurIPS, ICLR, CVPR, MLSys, SC/Supercomputing, etc.), GitHub, arXiv, Semantic Scholar, and domain-specific professional communities
Build separate, targeted pipelines per role rather than treating research profiles as interchangeable
Develop market intelligence on competitive talent landscapes in applied AI research and AI systems domains
Apply Boolean search, academic publication tracking, and community mapping to surface qualified passive candidates
Screen and assess candidates against distinct competency profiles: evaluation methodology and qualitative research skills for evaluation roles; controllability, RLHF, and personalization depth for scientist/engineer roles; distributed systems, compiler optimization, and training stack experience for systems roles
Read abstracts, publication histories, and project descriptions to assess topical relevance to specific open roles
Calibrate with the full-cycle recruiting team and hiring managers to understand search requirements and adjust sourcing strategy accordingly
Deliver detailed, substantive candidate write-ups that speak to research fit and domain alignment, not just resume summary
Maintain high standards for candidate quality over volume
Manage concurrent sourcing workstreams across Applied AI and Systems Research verticals independently
Track sourcing metrics per role and provide regular updates on pipeline health, candidate availability, and market conditions
Communicate proactively on pipeline status and adjust approaches based on recruiter and hiring manager feedback
Partner closely with full-cycle recruiters to ensure clean, well-documented handoff of qualified candidates
Build relationships with passive research candidates for near and long-term pipeline development
Support the broader recruiting function with market insights on AI research and systems talent trends