Job Url: https://www.indeed.com/jobs?q=developer&l=United+States&sc=0kf%3Aattr%28DSQF7%29%3B&radius=50&fromage=3&start=100&vjk=5f21d3203b5b0d50 Job Description: Infrastructure Engineer - Software Engineer – Infrastructure & Hardware Optimization - Remote- job post Cystems Logic Inc 4.5 4.5 out of 5 stars Houston, TX•Remote Contract Cystems Logic Inc Houston, TX•Remote Apply now Profile insights Here’s how the job qualifications align with your profile. Skills Systems engineering Linux Kubernetes + show more Do you have experience in Systems engineering? Yes No Skip   Job details Here’s how the job details align with your profile. Job type Contract   Full job description Job Description Hello, Infrastructure Engineer - Software Engineer – Infrastructure & Hardware Optimization - Remote We have below job opening. If you are interested and your experience match with job description. Please send your updated resume....Asap Software Engineer – Infrastructure & Hardware Optimization Location: SF, CA, Portland, OR, Dallas, TX - Remote but need to be local of respective location Duration: 6 Months+ Contract Job Description: We are seeking a skilled low-level systems engineer to join the team. This individual will focus on infrastructure software that detects, configures, and optimizes AI inference pipelines across heterogeneous hardware accelerators (e.g., NVIDIA / AMD GPUs, TPUs, AWS Inferentia, FPGAs). You will work on hardware abstraction layers, containerized runtime environments, benchmarking, telemetry, and driver orchestration logic for multi-cloud agentic inference deployments. Ideal Experience: 4–7 years experience in systems software or infrastructure engineering, preferably with exposure to AI/ML workloads. Deep expertise in CUDA, NCCL, ROCm, or other accelerator programming frameworks. Familiarity with LLM inference runtimes (TensorRT-LLM, vLLM, ONNXRuntime). Experience with Kubernetes scheduling, device plugin development, and runtime patching for heterogeneous compute. Strong Python/C++ and Linux systems programming skills. Passion for building scalable, portable, and secure AI infrastructure. Responsibilities: Design and implement cross-platform hardware detection systems for GPUs/TPUs/NPUs using CUDA, ROCm, and low-level runtime interfaces. Build and maintain plugin-based infrastructure for capability scoring, power efficiency tuning, and memory optimization. Develop hardware abstraction layers (HAL) and performance benchmarking tools to optimize AI agents for cloud-native inference. Extend container-based MLOps systems (Docker/Kubernetes) with support for hardware-specific runtime containers (e.g., TensorRT, vLLM, ROCm). Automate driver validation, container security hardening, and runtime health monitoring across deployments. Integrate telemetry systems (Prometheus, Grafana) to surface per-device inference performance metrics and health status. Collaborate with solutions and DevOps teams to ensure hardware-aware agent deployment across cloud providers. Additional Information All your information will be kept confidential according to EEO guidelines.   If you require alternative methods of application or screening, you must approach the employer directly to request this as Indeed is not responsible for the employer's application process. Report job