Technical Sourcer
Technical Sourcer owning top-of-funnel for hard-to-fill engineering and science roles (CUDA/C++ research engineers, SREs). Build pipelines via advanced research across labs/HPC/quant/PhD channels, personalized outreach, and AI tooling experiments. Requires 3+ years technical sourcing on niche profiles, strong writing/metrics, and onsite in SF.
About the job
Responsibilities
- Build and qualify pipelines for hardest technical searches (engineering and science roles), owning top-of-funnel end-to-end: market mapping, channel development, outreach, and first qualification.
- Work directly with Talent team and hiring managers.
- Map markets for research-engineer searches across labs, HPC, quant, and PhD-exit channels; turn into living pipeline validated with hiring managers.
- Execute personalized, content-led outreach campaigns that convert into screens.
- Build lasting infrastructure: talent pools, nurture sequences, clean tracking, new channels.
- Optimize own funnel with data-driven root cause analysis.
- Experiment with AI-native sourcing tools (semantic search, enrichment, automation) to find alpha.
Requirements
- 3+ years of technical sourcing on hard searches (niche engineering, research, or infrastructure profiles).
- Advanced research craft: GitHub, papers, patents, conference talks, communities (beyond LinkedIn filters).
- Demonstrable outreach writing that converts (show messages and metrics).
- Data-driven practice: can diagnose and fix funnel issues.
- Self-prioritization without supervision.
- AI-native approach: experiments with semantic search, enrichment, automation tools.
- Curiosity and willfulness.
- Able to work 5 days/week onsite in San Francisco office.
Nice-to-Haves
- Experience turning sourcing into full-cycle recruiting as company scales.
- Interest in Causal AI, CUDA/C++, supercomputing, or related technical domains.
Skills
Technical Sourcing, Market Mapping, Outreach Writing, Boolean Search, Semantic Search, GitHub, Research Papers, Patents, Conference Talks, AI Tools, Talent Pipelining, Data Analysis
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