Role Description This health sciences-focused position offers an excellent opportunity for an experienced Senior level Research Informationist to provide expert research services to RAND’s geographically diverse world-class research and administrative staff. The primary focus of this position is health sciences and evidence synthesis, though the role contributes to general reference services across RAND’s broader research portfolio. As a member of the Library Research Services (LRS) team, the ideal candidate will have demonstrated success in providing high-level health sciences information services in a research or academic library setting, thrives in a collaborative and collegial environment, and demonstrates initiative and an innovative mindset. The position reports to the Director, Knowledge Services and may work fully remote. Duties and Responsibilities - Provide advanced, complex consulting and research services to RAND research and administrative staff; partner with research staff on short- and long-term projects, providing an array of research and consulting services. - Perform systematic reviews and literature searches, and compile bibliographies and other outputs using electronic resources and commercial databases; prepare associated literature reviews, systematic search documentation, and annotated bibliographies. - Lead evidence synthesis intake and protocol review processes; provide training and consultations on systematic and scoping review methods for project teams. - Assist researchers with proposal development. - Develop and deploy customized information and current awareness plans. - Evaluate and apply emerging technologies, including AI-assisted tools, to enhance research workflows and information services. - Identify and recommend best literature review and evidence synthesis methods and tools for use by project teams. - Cultivate client relationships and facilitate collaboration in a hybrid environment to ensure resources and services meet demonstrated need. - Participate in teams to develop and implement effective and innovative outreach activities and service enhancements. - Participate in projects to develop relevant collections of electronic resources; evaluate new and existing resources; analyze collection metrics. - Provide support, consultations, customized training and educational sessions on evidence synthesis and literature reviews; provide training to more junior level staff. - Other duties as assigned. Skills and Abilities - Demonstrated expertise in performing comprehensive systematic reviews, including conducting search strategies in biomedical, nutrition, and public health related databases using MeSH, Emtree, keyword, Boolean and proximity searches. - Demonstrated knowledge of reporting guidelines and/or standards such as PRISMA, PRESS, Cochrane, etc. - Experience searching grey literature on health-related and other topic areas. - Experience with AI tools for evidence synthesis (e.g., Elicit, Covidence, DistillerSR) and ability to critically evaluate AI-generated search strategies against methodological standards. - Proficiency using EndNote; knowledge of other citation management tools. - Excellent client service skills. - Excellent attention to detail, ability to prioritize tasks, work independently, and meet a high standard of work. - Ability to work collaboratively in a team environment, to solve problems creatively, and build effective partnerships using excellent communication, interpersonal, and problem-solving skills. - Adheres to evidence synthesis best practices and professional standards for search strategy development, documentation of methodology, and quality assurance of project deliverables. Qualifications - Minimum of 7 years providing reference and consulting services in an academic or special library setting, preferably with a health-sciences focus. - Demonstrated expertise in information discovery and management tools in the health sciences (e.g., PubMed, Embase, Cochrane, and CINAHL), as well as proficiency searching general commercial bibliographic databases and platforms (e.g., Ebsco, ProQuest, etc.). - Experience working with faculty or professional research staff in either an academic or corporate environment. - Excellent written and verbal communication skills, with the demonstrated ability to present information to groups. Preferred - Experience with Alma/Primo library systems platform. - Study or degree in an area of health sciences. - Secondary experience or background in one or more of the following subject areas: national security, science/technology, economics, political science, international relations, education and labor, or other areas of RAND research. - AHIP membership and/or MLA SRSS Certifications. Education - MS/MA in library/information science. Location - This position is a fully remote opportunity. Term - This position will be a 1-year term duration. Salary Range - $102,800 - $156,500 (Full Time Annualized). Benefits - RAND considers a variety of factors when formulating an offer, including but not limited to, the specific role and associated responsibilities; a candidate’s work experience, education/training, skills, expertise; and internal equity. - The salary range includes base pay plus RAND’s sabbatic pay (which provides additional compensation above base pay when vacation is taken). - In addition, RAND provides strong benefits including health insurance coverage, life and disability insurance, savings plan, paid time-off and more. Equal Opportunity Employer

Role Description Meta is seeking Research Engineers to join Fundamental AI Research (FAIR). We are committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world. We are seeking individuals passionate in solving systems challenges to sustainably accelerate our reach to human-level intelligence. Candidates will have an opportunity to make fundamental advances in systems and apply their ideas at an unprecedented scale. The mission of Meta FAIR's SysML research is to advance the state of AI through open science innovations. We explore, design, and build ML systems and infrastructures at scale with usability, efficiency, and sustainability as design principles. Some aspects of this role include: - Enabling distributed training at an unprecedented scale through advancements and development in training library and authoring components, such as cuBLAS, cuDNN, FlashAttention. - Training performance acceleration through hardware-software co-design. - Carry out cutting-edge research to advance the science and technology of machine learning systems. - Perform research that enables learning the semantics of data (images, video, text, audio, and other modalities). - Devise better data-driven models of AI system design and optimization. - Contribute research that leads to innovations in: - Scalable machine learning systems. - Resource-efficient AI data and algorithm scaling. - Neural network architectures. - Memory and energy-efficient AI systems. - Environmentally-sustainable AI system and hardware designs. - Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results. - Publish research results and contribute to research that impacts Meta product development. Qualifications - Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. - Master's degree in the field of Computer Science, Computer Engineering, or equivalent practical experience. - 4+ years of domain-specific industry experience in areas related to development in systems, computer architectures, compiler and programming languages, machine learning, and artificial intelligence. - Experience with Python, C++, C, Rust or other related languages and with PyTorch framework. - Experience developing and optimizing systems for at-scale machine learning execution. - Experience devising data-driven models and real-system experiments and design implementation for AI system optimization. - Experience with scalable machine learning systems, resource-efficient AI data and algorithm scaling, or neural network architectures. - Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward. - Experience working and communicating cross functionally in a team environment. Preferred Qualifications - PhD in the field of Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. - Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies. - Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews). - Demonstrated research and software engineering experience via work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub). - Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, journals or conferences such as MLSys, ISCA, ASPLOS, HPCA, PLDI, CGO, NeurIPS, ICML, ICLR, or similar. - Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements). Benefits - $154,003/year to $217,000/year + bonus + equity + benefits.

Role Description The Training Infrastructure team at Luma is responsible for building and maintaining the distributed systems that enable training of our large-scale multimodal models across thousands of GPUs. This team ensures our researchers can focus on innovation while having access to reliable, efficient, and scalable training infrastructure that pushes the boundaries of what's possible in AI model development. We are looking for engineers with significant experience solving hard problems in PyTorch, CUDA and distributed systems. You will work alongside the rest of the research team to build & train cutting edge foundation models on thousands of GPUs that are built to scale from the ground up. Responsibilities - Design, implement, and optimize efficient distributed training systems for models with thousands of GPUs - Research and implement advanced parallelization techniques (FSDP, Tensor Parallel, Pipeline Parallel, Expert Parallel) - Build monitoring, visualization, and debugging tools for large-scale training runs - Optimize training stability, convergence, and resource utilization across massive clusters Qualifications - Extensive experience with distributed PyTorch training and parallelisms in foundation model training - Deep understanding of GPU clusters, networking, and storage systems - Familiarity with communication libraries (NCCL, MPI) and distributed system optimization - (Preferred) Strong Linux systems administration and scripting capabilities - (Preferred) Experience managing training runs across >100 GPUs - (Preferred) Experience with containerization, orchestration, and cloud infrastructure Company Description Luma’s mission is to build unified general intelligence that can generate, understand, and operate in the physical world. - We believe that multimodality is critical for intelligence. - To go beyond language models and build more aware, capable and useful systems, the next step function change will come from vision. - We are working on training and scaling up multimodal foundation models for systems that can see and understand, show and explain, and eventually interact with our world to effect change.

• Own the path from research output to production-ready artifact: a detection rule, a distributed feed, a scoring input, or a customer alert • Build and maintain STRIKE platform components across multiple services and runtimes • Turn research into shipped detection content: YARA, Sigma, STIX patterns, behavioral indicators, and the pipelines that distribute them • Drive STIX 2.1 adoption as a unified output schema and TAXII 2.1 as a distribution standard • Build the automation that removes commodity overhead from research work • Coordinate with engineering, measurement, and platform product teams so research actually lands in product