TMSfirst

Application Engineer

• 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.

Senior Application Engineer

Product Marketing

• 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.

Senior Technical Product Marketing Manager

Artificial Intelligence

• 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.

Senior/Principal AI Performance Engineer

Engineer

• 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.

Senior Application Engineer

Senior Technical Product Marketing Manager

Senior/Principal AI Performance Engineer

Senior/Principal Performance Engineer