periodic-labs - Technical Sourcer - physical sciences

• Own full-cycle sourcing across technical roles spanning AI/ML research, software infrastructure, and physical sciences (materials, chemistry, physics, lab engineering) • Build and manage diverse, high-quality talent pipelines using LinkedIn Recruiter, GitHub, Google Scholar, academic databases, conference publications, and domain-specific communities • Craft compelling, personalized outreach that resonates with highly specialized candidates who aren’t actively looking — and convert them into engaged prospects • Partner closely with hiring managers and researchers to deeply understand role requirements, ideal profiles, and the nuances that separate a great candidate from a good one • Develop and maintain sourcing strategies for hard-to-fill roles, including niche scientific disciplines and emerging fields at the intersection of AI and physical science • Track pipeline health and sourcing metrics in Ashby (our ATS), including outreach response rates, conversion rates, and time-to-pipeline, and surface insights proactively • Continuously research the competitive landscape — knowing where top talent works, studies, and publishes, and staying ahead of hiring trends in both AI and deep tech • Attend conferences, research symposia, and recruiting events to build Periodic Labs’ presence and network in key talent communities • Collaborate with the recruiting coordinator and Head of Recruiting to ensure sourced candidates have a seamless handoff and excellent early experience • Contribute to employer branding efforts by helping articulate what makes Periodic Labs unique to candidates across scientific and engineering disciplines • YOU WILL THRIVE IN THIS ROLE IF YOU HAVE • 5+ years of sourcing or recruiting experience, with a focus on technical roles in software, AI/ML, or deep tech • A proven track record of building pipelines for hard-to-fill, highly specialized roles — including research scientists, engineers, or other niche technical profiles • Deep familiarity with sourcing tools and platforms: LinkedIn Recruiter, GitHub, Google Scholar, Semantic Scholar, ResearchGate, and Boolean search strategies • Comfort reading and evaluating technical profiles across domains you may not have formal training in — you’re curious enough to learn the difference between an ML researcher and an MLOps engineer, or between a materials scientist and a chemical engineer • Strong written communication skills and a talent for crafting outreach that feels personal, specific, and worth responding to • Experience working with an ATS (Ashby, Greenhouse, Lever, or similar) and a track record of maintaining clean data and pipeline hygiene • High agency and comfort with ambiguity — you can build sourcing strategies from scratch when no playbook exists • Genuine curiosity about science and technology, even if you’re not a scientist yourself • ESPECIALLY STRONG CANDIDATES MAY ALSO HAVE • Experience sourcing for physical sciences roles: materials scientists, chemists, physicists, lab engineers, or hardware/instrumentation specialists • Familiarity with academic recruiting pipelines — PhDs, postdocs, and faculty — and how to engage researchers who aren’t on traditional job boards • Experience at an AI lab, deep tech company, national lab, or research institution • Knowledge of the AI/ML talent landscape, including key research groups, university programs, conferences (NeurIPS, ICML, ICLR), and where top practitioners tend to cluster • Experience using sourcing tools like Apollo, SeekOut, Findem, or similar AI-assisted platforms • A background in recruiting coordination, executive search, or research operations that brings additional context and rigor to candidate engagement • Minimum education: Bachelor’s degree or an equivalent combination of education and training or experience • Location: Our lab is located in Menlo Park and we prefer folks to be located in Menlo Park or San Francisco but can be flexible based on role