Senior Infrastructure Engineer responsible for building and maintaining software tools, automation, and data platforms that support hardware engineering lifecycle at Crusoe.
Compensation
Compensation will be paid in the range of $170,000 - $205,000. Restricted Stock Units are included in all offers.
Responsibilities
Design, build, and maintain software tools and frameworks for hardware development and sustaining lifecycle
Develop automated test, diagnostics, and health‑monitoring frameworks for large‑scale server fleets
Build tooling, monitoring and logging systems for hardware engineers
Create automated validation and characterization workflows for GPU, CPU, and high‑performance compute platforms
Build and operate data pipelines, dashboards, and analytics for hardware failure trends
Requirements
Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience
Nice to have: GPU clusters
Nice to have: GPU-to-GPU communication diagnostics
Lead performance optimization for large language model training and inference on NVIDIA GPU clusters, improving GPU utilization, memory footprint, and distributed training efficiency.
Locations (2)
Cambridge, Massachusetts, United States
San Francisco, California, United States
Compensation
Expected Base Salary Range
$148,000—$240,000 USD
Responsibilities
Profile and optimize GPU compute kernels for large language models
Optimize GPU cluster performance for collective/MoE frameworks and RDMA/IB/EFA usage
Manage big data pipelines for mid-training scale data
Optimize reinforcement learning pipelines and async cycles
Ensure optimized configurations are tested, accessible, and adaptable for research tasks
Requirements
Bachelor's or Master's in Computer Science or related quantitative field
Lead end‑to‑end engineering of Dell networking products and solutions for data center and AI fabric environments.
Locations (2)
Chennai, Tamil Nadu, India
Bangalore, Karnataka, India
Responsibilities
Lead the end‑to‑end engineering of Dell networking products and solutions for data center and AI fabric environments
Collaborate with platform, silicon, software, validation, solutions, and customer‑facing teams to deliver next‑generation Dell Enterprise SONiC capabilities
Own complex networking features from requirements and architecture through validation and delivery of production‑quality Dell Enterprise SONiC software
Architect and deliver large‑scale data center and AI fabric capabilities on Dell PowerSwitch platforms
Troubleshoot complex system, scalability, performance, and customer issues across the control plane, data plane, SAI, and switch ASIC layers
Lead end‑to‑end engineering of Dell networking products and solutions for data center and AI fabric environments.
Locations (2)
Chennai, Tamil Nadu, India
Bangalore, Karnataka, India
Responsibilities
Lead the end‑to‑end engineering of Dell networking products and solutions for data center and AI fabric environments
Collaborate with platform, silicon, software, validation, solutions, and customer‑facing teams to deliver next‑generation Dell Enterprise SONiC capabilities
Own complex networking features from requirements and architecture through validation and delivery of production‑quality Dell Enterprise SONiC software
Architect and deliver large‑scale data center and AI fabric capabilities on Dell PowerSwitch platforms
Troubleshoot complex system, scalability, performance, and customer issues across the control plane, data plane, SAI, and switch ASIC layers
We are seeking a highly skilled ML/AI Engineer to join our team to lead and support benchmarking of GPU platforms for machine learning and AI workloads.
Locations (2)
Amsterdam, North Holland, Netherlands
United States
Responsibilities
Work closely with hardware, development teams to profile and analyse GPU performance at the system and kernel level.
Evaluate and compare GPU performance across different platforms, architectures, and software stacks (e.g.,CUDA, ROCm).
Debug and optimise ML workloads to run efficiently on GPU hardware, identifying and resolving performance bottlenecks.
Perform acceptance testing for new GPU clusters, ensuring hardware and software meet performance, stability, and compatibility requirements for AI workloads.
Perform experiments across diverse GPU system configurations to assess the impact of varying interconnect strategies and system-level optimisations on performance and scalability.
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