Staff Hardware Systems Engineer, Crusoe Energy Systems

• 8+ years of experience in hardware systems engineering, platform engineering, performance engineering, ML systems engineering, infrastructure engineering, or related areas
• Hands-on experience with large-scale GPU or accelerated computing infrastructure for AI/ML or HPC workloads
• Hands-on experience with distributed training and/or inference workloads at scale, including parallelism strategies and performance tuning across the hardware/software stack
• Experience with workload benchmarking, performance profiling, and system performance optimization across hardware and software layers
• Strong understanding of modern server and accelerator architectures, including CPU, GPU, memory, storage, networking, and high-speed interconnects such as PCIe, InfiniBand, or NVLink
• Hands-on experience with system bring-up, validation, performance characterization, and root-cause analysis of complex hardware/software issues
• Experience developing automation, testing, diagnostics, or data-analysis frameworks using Python, Shell, or similar languages
• Ability to analyze system behavior using telemetry, benchmarks, profiling tools, and other quantitative data
• Experience working across multiple engineering disciplines, including hardware, firmware, software, networking, and infrastructure teams
• Strong analytical and problem-solving skills with the ability to operate effectively in ambiguous and rapidly evolving environments
• Excellent technical communication skills and experience collaborating with internal engineering teams, customers, and external technology partners
• Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, Computer Science, or equivalent experience
• Background in sustainable or energy-efficient hardware design practices

Desirable

• We are seeking a Staff Hardware Systems Engineer to strengthen Crusoe’s Hardware Systems Engineering team and close critical skill gaps in debugging, validation, performance evaluation and production support of high-performance compute systems
• In this role, you will participate in the full hardware lifecycle – from prototype bring-up to large-scale production while driving automation, deep issue resolution, and reliability across Crusoe Cloud’s GPU- and CPU-based infrastructure
• You will be collaborating with hardware, software, infrastructure, and vendor engineering teams while working across platform bring-up, validation and performance characterization
• Your work will directly impact Crusoe’s ability to deploy and operate sustainable, AI-first compute systems with world-class performance and reliability
• Drive the end-to-end lifecycle of next-generation compute platforms, including evaluation, bring-up, validation, deployment, and production readiness
• Define and execute performance characterization and validation strategies for CPU, GPU, and accelerated computing platforms
• Conduct in-depth workload characterization studies across training and inference - dense, MoE, long-context, and multimodal models to understand compute, memory, communication, and I/O behavior on target platforms
• Translate workload and platform insights into cluster-level tuning and configuration recommendations: topology, parallelism strategy, scheduling, power, and software stack settings to maximize delivered performance and efficiency
• Build and maintain workload performance profiles and reference configurations that guide how clusters are deployed, tuned, and scaled for specific model families and workload classes
• Analyze system and workload performance, identify bottlenecks, and work across hardware and software layers to drive improvements
• Lead complex system-level debugging across compute, memory, storage, networking, accelerators, and platform firmware
• Partner with vendors and internal engineering teams on prototyping, qualification, NPI, and production readiness of new technologies
• Collaborate across hardware, firmware, networking, software, infrastructure, reliability, and operations teams to resolve complex platform issues
• Use data and system-level insights to influence platform architecture, technology selection, hardware roadmaps, and long-term infrastructure strategy