Technical Sourcer (contract, hyrbrid, MH)

Technical Sourcer

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. Whether they need help building recruiting processes, attracting top talent, or payrolling contractors, we can help.

Job Description

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.

Key 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

Candidate Evaluation & Qualification

• 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

Pipeline Management & Communication

• 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

Stakeholder Collaboration

• 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

Work Logistics

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

Qualifications

Required:

• 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

Preferred:

• 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

Additional Information

All your information will be kept confidential according to EEO guidelines.