Periodic Labs

• 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

• Post-train frontier models to autonomously run parts of the scientific discovery pipeline • Generate hypotheses and design experiments that run in an actual lab • Operate sophisticated scientific equipment • Create high-quality evaluation and training tasks • Scale up RL environments and design creative reward functions • Run large-scale RL runs for automating scientific discovery

• Join a world-class team of scientists and engineers pushing the boundaries of physics research in a groundbreaking lab where AI, theory, and automation unlock discoveries at unprecedented speed and scale. • Work on using theoretical modeling to connect first-principles calculations and experiments. • Collaborate with computational and experimental scientists and ML researchers.

• You will lead, design, build, and operate Periodic Labs’ Security and IT. • You’ll own identity, endpoint, network, and SaaS security. • You'll implement smooth, secure internal workflows and keep our researchers productive. • You’ll write automation, integrate systems, and set high standards for security, reliability, and user experience. • You’ll work closely with research, infra, and operations to ensure our environments including laptops, clusters, and science labs are secure, compliant, and fast.

• Train frontier models to be highly knowledgeable scientific experts • Develop methods for synthetic data generation, distillation, and continual learning at scale • Work closely with RL researchers, physicists, and chemists to create evals that guide scientific data curation • Collaborate with supercompute engineers to scale compute-efficient LLM training to thousands of GPUs • Build high-performance tools for investigating how data shapes intelligence

• Lead, design, build, and operate large-scale compute clusters to power AI scientific research. • Write software that orchestrates large GPU and CPU clusters, manages resource allocation and automates cluster lifecycle operations. • Work on bringup, operations and maintenance of all aspects of these clusters. • Build tools and get directly involved in large scale frontier research experiments.

• Integrate, optimize, and operate large-scale inference systems to power AI scientific research • Build and maintain high-performance serving infrastructure that delivers low-latency, high-throughput access to large language models across thousands of GPUs • Work closely with researchers and engineers to integrate cutting-edge inference into large-scale reinforcement learning workloads • Build tools and directly support frontier-scale experiments to make Periodic Labs the world’s best AI + science lab • Make contributions to open-source LLM inference software

• Optimize, operate and develop large-scale distributed LLM training systems • Work closely with researchers to bring up, debug, and maintain mid-training and reinforcement learning workflows • Build tools and directly support frontier-scale experiments • Contribute open-source large scale LLM training frameworks