TMSfirst

• As a Sr. Application Engineer, you will serve as a key contributor to the development and expansion of Ascender Pro and other internal application projects. • You will take ownership of engineering work across both frontend and backend systems, building and shipping features at a high velocity. • Design, develop, and maintain application features using Python (backend) and React/TypeScript (frontend). • Contribute to additional projects built in Go as needed. • Leverage AI-assisted development tools effectively while maintaining the judgment to validate and correct AI-generated output. • Collaborate closely with the existing engineering team to ship production-quality code at a fast pace. • Participate in architecture decisions and help define best practices for the application stack. • Troubleshoot, debug, and resolve issues across the full stack. • Support other internal engineering initiatives as bandwidth allows.

• Author long-form technical blog posts, white papers, solution briefs, tutorials, and case studies that showcase product capabilities and thought leadership. • Produce executive summaries and digestible overviews of complex technical topics for C-suite, business, and non-technical audiences. • Develop content across the full buyer journey - awareness, consideration, and decision, ensuring technical accuracy and marketing impact at every stage. • Write and edit website copy, landing pages, email campaigns, and social media content with a technically grounded voice. • Partner with engineering, product management, and solution architecture teams to extract technical insights and convert them into audience-appropriate messaging. • Attend sprint reviews, architecture discussions, and product demos to stay current on product capabilities, roadmap, and differentiators. • Translate command-line workflows, system architectures, benchmark data, and configuration details into clear, visually supported narratives. • Own and maintain the technical content calendar in coordination with product marketing, demand generation, and communications teams.

• Design, develop, and maintain comprehensive benchmarking frameworks spanning OS, kernel, and application layers. • Profile workloads across CPU, memory, I/O, network, and accelerator (GPU/NPU) subsystems to identify bottlenecks and optimization opportunities. • Establish and own performance baselines across CIQ's product and solutions portfolio. • Leverage AI-assisted tooling and agentic workflows to accelerate profiling, analysis, and root cause identification. • Build and maintain automated performance regression-detection pipelines integrated into CI/CD workflows using Fuzzball. • Identify, triage, and resolve regressions across user space, kernel space, and application layers with urgency and rigor. • Collaborate across engineering teams to root-cause regressions introduced by upstream kernel changes, compiler updates, or library modifications. • Drive proactive performance improvements - not just reactive fixes - to keep CIQ solutions ahead of the competition across every layer of the stack. • Own core operating system performance: kernel subsystem tuning (scheduler, memory management, I/O, networking), system call overhead reduction, and user space library and runtime optimizations. • Identify and implement kernel-level enhancements, including patches, configuration changes, and upstream contributions that yield measurable performance gains for CIQ's customer workloads. • Optimize for AI inference and training workloads, including LLM serving, model parallelism, and accelerator utilization. • Tune performance for HPC workloads, including modeling, simulation, and tightly coupled parallel applications (MPI, OpenMP, etc.). • Optimize general computing and service workloads - web services, databases, messaging systems, and other production software that runs on CIQ's OS platform. • Work at all levels of the stack: compiler flags, kernel parameters, scheduler tuning, NUMA topology, memory allocation, and application-level algorithmic improvements. • Champion an AI-first engineering philosophy - use AI tools, agents, and automation to accelerate your own productivity and the quality of performance insights. • Identify and prioritize optimization opportunities that directly impact AI training throughput and inference latency/cost. • Stay current on state-of-the-art techniques in ML system performance, including quantization, batching strategies, kernel fusion, and hardware-software co-design. • Develop deep expertise in CIQ's Fuzzball platform - its architecture, scheduling, and workload execution model. • Integrate performance benchmarks, regression tests, and user-facing workloads into Fuzzball-based pipelines. • Contribute to the performance characterization of Fuzzball itself, ensuring the platform adds minimal overhead and scales efficiently. • Develop broad familiarity with the full CIQ product portfolio — including Rocky Linux and RLC (and its variants), Fuzzball, Apptainer (formerly Singularity), and Warewulf - understanding how performance considerations span and interconnect across each. • Collaborate deeply with the engineering teams behind each product line to surface, prioritize, and deliver performance improvements that benefit customers across the entire CIQ ecosystem. • Partner with product and customer success teams to translate real-world performance pain points into engineering priorities and measurable outcomes. • Document and communicate findings clearly - from low-level profiling data to executive-level summaries. • Contribute to technical publications, conference presentations, and thought leadership that reinforces CIQ's reputation for performance excellence.

• 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.

• Write and produce technical content across formats that practitioner audiences (developers, sysadmins, and infrastructure architects) actually read and trust: solution briefs, whitepapers, technical blogs, benchmark writeups, and data sheets. • Partner with engineering and product to surface technically rich stories (benchmark results, architecture decisions, integration patterns, customer outcomes) and turn them into content that supports buying decisions. • Apply established messaging and positioning consistently across all assets, maintaining accuracy and discipline across every format. • Manage production volume and editorial calendar in coordination with the broader marketing team. • Own CIQ's webinar program end-to-end: strategy, calendar, production, promotion coordination, and performance tracking. • Build and maintain the technical collateral that equips sales and sales engineering to run evaluations and win competitive deals.

• Showcase excellent communication and client management skills. • Analyze client organizations’ systems to identify weaknesses and risks. • Deliver clear, easy-to-understand assessments and provide tangible solutions. • Be part of the deal team, facilitating the sales process. • Organize regular client meetings to discuss their requirements. • Assist in troubleshooting and incident resolution in enterprise environments. • Create architecture solutions that create value for clients. • Participate in project meetings and discussions to capture key decisions and translate them into relevant documentation. • Stay up to date on new internal features and product launches. • Monitor and report on sales performance analytics. • Recommend innovative ideas to optimize sales.