• Lead and support the development of mathematical optimization models for energy networks, including formulation, implementation, solution, analysis, and communication. • Partner with other LLNL scientists and engineers to bring research results to practical use in LLNL Global Security programs. • Work closely with other CIR program area leads in developing the CIR program research roadmap. • Develop internal and external collaborations and provide input for securing funding sources to meet program goals and objectives. • Co-author refereed scientific papers and reports and deliver presentations for audiences internal and external to LLNL. • Mentor junior staff members. • Balance multiple projects/tasks and priorities of customers and partners to ensure deadlines are met. • Lead various highly complex projects, effectively leveraging the skills of team members. • Serve as a mentor for other staff and assist in recruiting efforts. • Serve as a primary technical point of contact with sponsors and partners and participate in development of new program business.

• Work closely with the research team to translate formal methods research into implemented solutions and integrate them into production-ready Code Metal products. • Apply and adapt tools and frameworks to make formal verification accessible to practicing engineers. • Conduct experiments to evaluate the applicability and effectiveness of formal methods on industrial-scale codebases. • Collaborate across multidisciplinary teams, contributing to both research initiatives and production-grade solutions.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a detail-oriented intern to provide administrative, content, and quality improvement support for grant-funded educational events. This role is ideal for a highly organized individual with strong proofreading skills who is comfortable working independently in a remote environment. The intern will support event preparation, execution, and post-event reporting. Must be a current Lipscomb University student, may be federal work study eligible. Key Responsibilities - Proofread and refine educational cases and facilitator guides developed by program leadership - Assist in translating case content into clear, structured scripts for standardized patient actors - Design and maintain event schedules - Provide logistical support for virtual events (approximately four per semester, conducted via Zoom) - Track and record participant attendance following events - Design and distribute event completion certificates - Assist with the design, distribution, and interpretation of participant quality improvement (QI) surveys - Gather and organize data and documentation for reports required by the funding agency Qualifications - Proficiency with Microsoft Office (Word, Excel, PowerPoint) or equivalent productivity software - Strong proofreading, editing, and attention-to-detail skills - Excellent organizational and time management abilities - Clear written communication skills - Comfort working with virtual platforms (e.g., Zoom, online surveys) How to Apply Interested candidates should submit a brief resume and a short statement of interest highlighting relevant skills and availability.

• Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents. • Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs. • Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks. • Design evaluation frameworks and metrics for agent behavior, analyze traces and logs, and close the loop from evaluation back into training, data, and reward design. • Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets. • Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets. • Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features.