Role Description At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget. The Physical and Computational Sciences Directorate's (PCSD’s) strengths in experimental, computational, and theoretical chemistry and materials science, together with our advanced computing, applied mathematics and data science capabilities, are central to the discovery mission we embrace at PNNL. The Advanced Computing, Mathematics, and Data Division (ACMDD) focuses on basic and applied computing research encompassing: - Artificial intelligence - Applied mathematics - Computing technologies - Data and computational engineering Our scientists and engineers apply end-to-end co-design principles to advance future energy-efficient computing systems and design the next generation of algorithms to analyze, model, understand, and control the behavior of complex systems in science, energy, and national security. Responsibilities - Data Preparation & Analysis: - Clean, transform, and integrate structured and unstructured datasets and documents, including equipment datasheets and ArcGIS-based spatial data, from environmental and energy systems domains. - Power Grid Systems, Simulation & Resilience: - Knowledge of power grid systems and resilience, with experience applying agentic AI to model power grid simulations, analyze grid-related problems, and support more efficient and reliable operational decision-making. - Data Engineering & Pipeline Architecture: - Design scalable data systems (ETL, data lakes, data meshes) that support large-scale legislation, regulatory, and legal document workflows. - Software Engineering & DevOps: - CI/CD, testing, version control, and open-source practices enabling reliable, reproducible systems across research and production. - Computer Vision & Image Analysis: - Develop and apply computer vision methods for crystallography workflows and metadata processing, including image analysis, object detection, tracking, segmentation, and automated interpretation, to enable automation of light source beamline operations in scientific and real-world environments. - Generative AI & Foundation Models: - Development and evaluation of large-scale generative AI systems, including fine-tuning, agents, tool use, and optimization for robustness, alignment, and efficiency. - Agentic AI Systems & Autonomous Workflows: - Design and deploy scalable agentic AI systems with autonomous workflows, dynamic tool use, and complex decision-making to support advanced scientific and operational tasks. Qualifications - Minimum Qualifications: - Candidates must have received a Master’s degree within the past 24 months or within the next 8 months from an accredited college or university. - Preferred Qualifications: - Masters degree in Computer Science, Data Science, Applied Mathematics, Electrical Engineering, Computational Engineering, or a closely related field. - Prior research experience, including internship experience within a national laboratory setting. - Experience in data preparation, integration, and applications involving large language models. - Strong technical computing skills in Python, with proficiency in NumPy or similar scientific computing frameworks. - Ability to translate ambiguous or evolving technical objectives into clear, actionable analytical approaches. - Experience working in applied research settings or collaborative R&D environments. - Demonstrated ability to work effectively within interdisciplinary and geographically distributed teams, using a collaborative and solutions-oriented approach. Benefits - Medical insurance - Dental insurance - Vision insurance - Robust telehealth care options - Several mental health benefits - Free wellness coaching - Health savings account - Flexible spending accounts - Basic life insurance - Disability insurance* - Employee assistance program - Business travel insurance - Tuition assistance - Relocation - Backup childcare - Legal benefits - Supplemental parental bonding leave - Surrogacy and adoption assistance - Fertility support - Company-funded pension plan* - 401(k) savings plan with company match* - Up to 120 vacation hours per year - Ten paid holidays per year Company Description Pacific Northwest National Laboratory (PNNL) is a world-class research institution powered by a highly educated, diverse workforce committed to the values of Integrity, Creativity, Collaboration, Impact, and Courage. Every year, scores of dynamic, driven people come to PNNL to work with renowned researchers on meaningful science, innovations and outcomes for the U.S. Department of Energy and other sponsors. PNNL is located in eastern Washington State—the dry side of Washington known for its stellar outdoor recreation and affordable cost of living. The Lab’s campus is only a 45-minute flight (or ~3 hour drive) from Seattle or Portland, and is serviced by the convenient PSC airport, connected to 8 major hubs.

• Build AI‑powered automations for core People workflows (recruiting, onboarding, offboarding, employee lifecycle, compliance) • Translate defined workflow specs or process maps into built, tested, and deployed automations • Build and deploy skills or agentic workflows in partnership with the AI COE, including work tied to PROG’s active PIPER+ program • Use AI tools (Claude, Microsoft Copilot, GitHub Copilot, or similar) as core building tools — for workflow logic, automation, and agent design • Integrate automations with Workday, Jira, Microsoft 365, and other tools in the PROG stack (API and MCP support provided) • Ship quickly and iterate: deliver functional MVPs, gather feedback, and improve over time • Maintain and enhance automations as People needs evolve • Document what you build so others can understand, maintain, and extend it • Partner closely with TA, People Ops, Total Rewards, and HR Technology leaders

Role Description We are looking for a talented student with a strong background in artificial intelligence to develop an AI agent capable of creating Class 1, 2, and 3 Natural Product Number (NPN) applications. The role also involves analyzing third-party testing and quality reviews of our nutraceutical products. - Develop an AI agent to automate the creation of Class 1, 2, and 3 NPN applications. - Collaborate with regulatory experts to ensure compliance with industry standards. - Implement machine learning algorithms to analyze third-party testing data and quality reviews. - Work with cross-functional teams to integrate AI solutions into existing workflows. - Document processes and provide insights for continuous improvement. Qualifications - Currently pursuing or recently graduated with a degree in Computer Science, Artificial Intelligence, Data Science, or a related field. - Strong understanding of AI and machine learning concepts. - Experience with programming languages such as Python, R, or Java a plus. - Familiarity with regulatory requirements in the nutraceutical industry is a plus. - Excellent problem-solving skills and attention to detail. - Ability to work independently and as part of a team. Benefits - Gain hands-on experience in AI development within the nutraceutical industry. - Opportunity to work on cutting-edge projects with industry experts. - Flexible working hours and potential for remote work. - Mentorship and professional development opportunities.

• Generate visual concepts and variations using generative AI tools as a standard first step in ideation. • Use AI to compress timelines — what used to take a week should take a day; what took a day should take an hour. • Know which tasks AI handles well and which require your full manual craft - and make that call without being asked. • Stay current as AI design tooling evolves; evaluate and introduce new tools when they raise the quality bar. • Website Design (xtm.ai) • Web Assets: Create all UI elements, page layouts, and visual components for the xtm.ai website - banners, hero sections, icons, illustrations, and supporting visuals. • Handoff: Deliver assets in formats ready for the HubSpot CMS handoff to the HubSpot & Web Operations Specialist. • CRO Awareness: Design with conversion in mind - understand the goal of every page before you design it. • ✦ AI Prototyping: Use AI tools to rapidly prototype layout variations and visual concepts before committing to full execution. • ✦ Generative Visuals: Generate imagery, illustrations, icons, and textures using AI image tools - refined and adapted to brand standards. • Campaign Materials • This is a high-volume production role. You are responsible for every visual asset that goes out the door. • Paid Ads: Design paid ad creatives across Google Display, LinkedIn, and any other active channels - multiple sizes and variants per campaign. • Social Media: Create all organic social media visuals — posts, stories, carousel formats, cover images. • Landing Page Visuals: Design landing page visuals for each campaign and deliver them for HubSpot build. • Event Materials: Produce event and webinar promotional materials — banners, slide decks, digital signage. • ✦ Speed to Market: Use AI to generate multiple creative directions quickly, enabling faster stakeholder sign-off and faster time to launch. • ✦ Asset Adaptation: Apply AI image editing tools to adapt and extend existing assets rather than rebuilding from scratch. • Newsletters & Email Design • Email Templates: Design and produce all marketing email templates - newsletters, nurture sequences, product updates, event invites. • Modular Email Design: Build reusable email modules in HubSpot that the team can assemble without needing a designer for every send. • Rendering: Ensure email layouts are mobile-optimised and render correctly across major clients. • ✦ Personalised Variants: Use AI tools to generate personalised visual variants for segmented email campaigns. • Brand Consistency & Design System • Brand Stewardship: Maintain and evolve the xtm.ai visual identity across all touchpoints - web, email, social, ads, events. • Design System: Build and maintain a Figma design system — components, tokens, and guidelines - that the wider team can reference. • Scalable Templates: Create templates and reusable assets so that simple production tasks can be handled by non-designers in a pinch. • ✦ Scaled Output: Use AI tools to generate brand-consistent variations at scale — localised assets A/B test variants, seasonal adaptations.