What You’ll Be Working On
- 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.
What You’ll Bring to the Team
- 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.
Bonus Points
- 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.
Benefits
- Industry competitive pay
- Restricted Stock Units in a fast growing, well-funded technology company
- Health insurance package options that include HDHP and PPO, vision, and dental for you and your dependents
- Employer contributions to HSA accounts
- Paid Parental Leave
- Paid life insurance, short-term and long-term disability
- Teladoc
- 401(k) with a 100% match up to 4% of salary
- Generous paid time off and holiday schedule
- Cell phone reimbursement
- Tuition reimbursement
- Subscription to the Calm app
- MetLife Legal
- Company paid commuter benefit; $300 per month
Compensation Range: $170,000 - $205,000. Restricted Stock Units are included in all offers. Compensation to be determined by the applicants knowledge, education, and abilities, as well as internal equity and alignment with market data.
CrusoeSunnyvale, CA
