Senior Hardware Systems Engineer

Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster.

We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI.

We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services.

If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe.

We are seeking a Senior 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.

• 5-6+ 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.

• Experience influencing hardware or system configuration decisions based on workload performance data (e.g: HW/SW co-design, platform tuning studies).

• Deep experience with RDMA, RoCE, CXL, NVLink or fabric-level performance analysis.

• Experience with inference serving frameworks, training frameworks, or ML compiler/runtime stacks.

• Familiarity with both x86 and ARM-based server platforms.

• Experience building observability, diagnostics, or fleet-level performance and reliability systems.

• Experience introducing new compute technologies into production cloud or large-scale datacenter environments.

• Understanding of infrastructure efficiency, power, cooling, performance-per-dollar, or total cost of ownership considerations.

• Background in sustainable or energy-efficient hardware design practices.

• Advanced certifications or coursework in AI/HPC hardware systems.