• Build multi-agent benchmark tasks that require reading, analyzing, and synthesizing large document collections • Curate real-world research corpora — academic papers, case studies, technical reports — and design questions that require comprehensive analysis • Write structured ground-truth oracles (JSON) with specific, verifiable answers that prove the agent actually read the source material • Design LLM judge prompts that evaluate agent output field-by-field against the oracle • Create decomposition guides that split research across multiple parallel sub-agents (one per document, one per domain, then synthesis)

Role Description The Portable Radio Program is seeking a qualified CM/ILS SME who brings extensive experience in defense life-cycle logistics and configuration management for complex electronic and C4ISR systems. The ideal candidate will possess deep expertise in Integrated Logistics Support (ILS), product support strategy development, and sustainment planning, with a proven ability to analyze technical data, contracts, and system specifications to ensure effective and compliant logistics solutions. This individual will support end-to-end life cycle management activities, including: - Provisioning - Maintenance planning - Engineering change management - Configuration control They will collaborate across Integrated Product Teams (IPTs) and program stakeholders, demonstrating strong familiarity with Navy logistics processes and systems. Responsibilities - Review technical manuals, maintenance requirements, and engineering drawings to ensure correct logistics support is in place. - Be familiar with DoD logistics systems such as NALCOMIS or R-Supply for ordering, tracking, and reporting supply information. - Utilize the Navy’s Planned Maintenance System (PMS) and PMSMIS (Planned Maintenance System Management). - Prepare written correspondence and verbally report recommended improvements to product support and maintenance concepts and requirements. - Coordinate with Logistics Element Managers (LEMs) to ensure integrated logistics support (ILS) products are technically accurate. - Formulate provisioning plans, schedules, and supply support management plans for new programs, systems, and equipment. - Participate in Integrated Product Team (IPT) and technical exchange meetings. - Support Program Manager and delivery of Product Support Strategy (PSS) documentations, including sustainment metrics (Key Performance Parameters/Key System Attributes), risks, and Integrated Product Support (IPS) elements. - Assist the Program and team to draft, deliver, and maintain ILS products, including nomenclature requests. - Support management and execution of Engineering Change Proposals (ECPs), Technical Directives (TDs), and modifications. - Participate in the system engineering and technical review process. - Act as a logistics representative through all design phases including associated reviews, Configuration Control Boards (CCBs) and Change Implementation Boards (CIBs). - Prepare periodic status reports, coordinate and execute periodic staff meetings and technical interchange meetings, and develop plans, schedules, and executive program briefings. - Develop Quick Reference Guides. - Develop Training Support Packages for equipment such as radio sets to include curriculum development and training aides. - RCM Processes: Maintenance Requirement Card (MRC), Maintenance Index Pages (MIP). Qualifications - Bachelor’s degree. - Three (3) years of experience in defense life-cycle (acquisition) logistics support of electronic systems, including logistics principles, practices, and processes. - Experience shall include at least one (1) year of support with C4ISR or similar systems, including analyzing Engineering/Systems Management Data and developing Logistics Plans and Procedures. - Individual shall have Defense Acquisition Workforce Improvement Act (DAWIA) certification in Lifecycle [Acquisition] Logistics Foundational/Level 1; demonstrate/specify equivalent life cycle logistics training; or possess an additional one (1) year (i.e., total 4 years minimum experience) working in direct support of defense life-cycle logistics. - Active Secret Clearance walking through the door. Requirements - Basic Compensation: $65,000 - $72,000 yearly salary. - The salary range listed reflects what we reasonably expect to pay for this role at the time of posting. The final offer may vary based on skills, experience, geographic location, market conditions, and internal equity. - Additional compensation may include performance incentives and program-specific awards. - We do not use salary history to determine compensation, in line with applicable law. Benefits - Personal Time Off (PTO) - Medical, dental, vision, supplemental life with AD&D - Short and long-term disability - Flexible spending accounts - Parental leave - Legal services - 401(k) Retirement Plan with a matching component - Training to help drive success, with access to a best-in-class e-learning suite for formal and informal learning, professional and technical certification preparation, and education assistance at accredited institutions.

• Design, implement, and tune inference pipelines for large language models and other AI workloads, targeting maximum throughput and minimum latency. • Apply state-of-the-art optimization techniques: quantization (INT4/INT8/FP8), model pruning, speculative decoding, continuous batching, and kernel fusion. • Optimize inference-serving stacks, including vLLM, TensorRT-LLM, ONNX Runtime, and similar frameworks, for production deployment on CIQ’s OS platform. • Profile and tune GPU/accelerator utilization across the full inference stack, from model weights and memory bandwidth to CUDA kernels and driver overhead. • Establish inference performance baselines and regression detection across CIQ’s AI-focused solutions. • Design and optimize distributed training pipelines for large-scale models, including data, model, tensor, and pipeline parallelism strategies. • Tune training efficiency through mixed-precision training, gradient checkpointing, activation recomputation, and optimizer-level improvements. • Benchmark training throughput and scaling efficiency across multi-GPU and multi-node configurations on CIQ’s infrastructure. • Collaborate with infrastructure and performance teams to resolve training bottlenecks at the network (RDMA/InfiniBand), storage, and OS layers. • Stay current on frontier model architectures and training techniques, including MoE models, RLHF pipelines, and emerging post-training methods. • Build and maintain a library of turn-key AI workload examples that run on CIQ’s platform, covering inference serving, fine-tuning, batch processing, RAG pipelines, and agentic workflows. • Develop both internal reference pipelines for CI/testing and customer-facing examples designed for immediate productivity on CIQ’s OS and Fuzzball. • Package workloads using containers to deliver portable, reproducible AI environments across HPC and cloud-native settings. • Create compelling, well-documented demos and reference architectures that communicate CIQ’s AI capabilities to technical and business audiences alike. • Partner with product and customer success teams to translate real-world AI use cases into reusable, production-quality examples. • Build and maintain AI-powered engineering tooling - leveraging LLM-based agents, automated analysis pipelines, and AI-assisted code generation to accelerate the broader engineering organization. • Champion an AI-first development culture: identify opportunities where AI tooling can reduce toil, surface insights faster, and improve software quality across CIQ’s products. • Evaluate and integrate emerging AI frameworks, libraries, and hardware as they become relevant to CIQ’s customers and product roadmap. • Contribute to open-source AI tooling and frameworks where relevant, reinforcing CIQ’s technical reputation in the community. • Develop deep expertise in CIQ’s Fuzzball platform, its architecture, scheduling model, and workload execution environment. • Integrate AI training, inference, and pipeline workloads into Fuzzball-based CI/CD and production pipelines. • Contribute to Fuzzball’s AI workload story: ensure the platform is a first-class environment for running AI workloads efficiently and at scale. • Help characterize and improve Fuzzball’s performance for AI-specific access patterns and resource demands. • Develop broad familiarity with the full CIQ product portfolio, including Rocky Linux and RLC (and its variants), Fuzzball, Apptainer, and Warewulf, and understand how AI workloads interact with each layer. • Collaborate closely with the Performance Engineering team to ensure AI workloads benefit from and contribute to CIQ’s systems-level optimization work.

• Design, develop, and deploy machine learning and deep learning models for scientific applications (e.g., materials discovery, chemical modeling, process optimization) • Translate complex scientific and business problems into tractable AI/ML frameworks • Work with real-world structured and unstructured scientific data (e.g., experimental, simulation, and literature data) • Build and maintain data pipelines, feature engineering workflows, and model evaluation frameworks • Collaborate cross-functionally with scientists, engineers, and product managers to deliver production-ready solutions • Partner closely with Customer Success and client-facing teams to understand customer needs, translate requirements into AI/ML solutions, and support the successful deployment, adoption, and ongoing optimization of models in customer environments • Apply techniques such as supervised/unsupervised learning, generative models, and optimization algorithms • Contribute to the integration of models into scalable software platforms and APIs • Stay current with advancements in AI/ML and relevant scientific domains; evaluate and apply new methods where appropriate • Perform strategic research oriented towards improving NobleAI’s core technology • Communicate findings and model outputs clearly to both technical and non-technical stakeholders • Periodic travel to customer sites and attend industry events