Designworks Talent LLC

Infrastructure Engineer

• Build and scale distributed training infrastructure supporting large AI models across large GPU clusters
• Design and improve systems that increase training reliability, efficiency, and resource utilization
• Develop solutions for fault tolerance, checkpointing, recovery, and large-scale training operations
• Integrate AI models into production training pipelines with platform, orchestration, and performance engineering teams
• Diagnose and resolve issues impacting training throughput, stability, reliability, and cost efficiency
• Build tools and automation that improve the developer experience for AI researchers and engineers
• Establish best practices for training infrastructure, operational processes, and platform reliability
• Contribute to the evolution of the AI infrastructure platform as an early engineering team member
• Collaborate with infrastructure, orchestration, performance, and machine learning teams

Senior AI Training Infrastructure Engineer

Engineer

• Design and build virtualization infrastructure supporting GPU-intensive AI and HPC workloads
• Develop and operate Kubernetes-based orchestration systems for GPU cluster provisioning and workload scheduling
• Build automated provisioning systems to allocate, scale, and reclaim GPU capacity efficiently across multiple tenants
• Design solutions for workload placement, resource management, and cluster lifecycle operations
• Partner with hardware, networking, infrastructure, and AI platform teams
• Improve the reliability, security, scalability, and operational maturity of the orchestration platform
• Build tooling and automation to simplify infrastructure management and improve developer and customer experiences
• Contribute to architectural decisions, engineering standards, and best practices
• Build the platform layer enabling customers to reliably consume GPU compute at scale across a multi-tenant AI cloud platform

Senior Virtualization & Orchestration Engineer

Network Engineer

• Architect, build, automate, and operate data center networking for large-scale GPU clusters, AI workloads, and multi-tenant cloud services
• Develop secure tenant isolation architectures for performance, scalability, and security
• Design resilient, highly available network architectures across compute, storage, and infrastructure services
• Work across networking platforms and vendors including Arista, Juniper, HPE, and other enterprise providers
• Partner with hardware, infrastructure, virtualization, and platform engineering teams to optimize networking for AI training and inference workloads
• Build infrastructure automation for deployment, configuration management, monitoring, and operational excellence
• Define networking standards, architecture documentation, and engineering best practices
• Evaluate emerging networking technologies and shape the long-term AI networking platform
• Own the design, automation, and evolution of the data center network
• Establish networking architecture and strategy as an early networking engineer

Staff Data Center Network Engineer

• Design and build virtualization infrastructure supporting GPU-intensive AI and HPC workloads
• Develop and operate Kubernetes-based orchestration systems for GPU cluster provisioning and workload scheduling
• Build automated provisioning systems to allocate, scale, and reclaim GPU capacity efficiently across multiple tenants
• Design solutions for workload placement, resource management, and cluster lifecycle operations
• Partner with hardware, networking, infrastructure, and AI platform teams
• Improve the reliability, security, scalability, and operational maturity of the orchestration platform
• Build tooling and automation to simplify infrastructure management and improve developer and customer experiences
• Contribute to architectural decisions, engineering standards, and best practices
• Own critical infrastructure systems end-to-end

• Build and scale distributed training infrastructure supporting large AI models across large GPU clusters
• Design and improve systems that increase training reliability, efficiency, and resource utilization
• Develop solutions for fault tolerance, checkpointing, recovery, and large-scale training operations
• Integrate AI models into production training pipelines with platform, orchestration, and performance engineering teams
• Diagnose and resolve issues impacting training throughput, stability, reliability, and cost efficiency
• Build tools and automation that improve the developer experience for AI researchers and engineers
• Establish best practices for training infrastructure, operational processes, and platform reliability
• Contribute to the evolution of the AI infrastructure platform as an early engineering team member

Staff AI Training Infrastructure Engineer

Hardware Engineer

• Lead the design and configuration of GPU servers and rack-scale infrastructure using vendor reference architectures from NVIDIA, AMD, and emerging AI hardware providers
• Partner with ODMs and OEMs to develop, validate, and optimize custom hardware platforms for production deployment
• Own rack-level architecture, including mechanical layout, power distribution, thermal design, cable management, serviceability, and deployment standards
• Evaluate new GPU platforms, server technologies, and hardware innovations for next-generation AI workloads
• Collaborate with data center engineering, networking, infrastructure, and operations teams on facility and production integration
• Balance performance, reliability, manufacturability, scalability, and total cost of ownership
• Support hardware validation, qualification, and production readiness for large-scale deployments
• Establish engineering standards, hardware specifications, and design best practices
• Influence long-term hardware architecture and infrastructure strategy

Principal Hardware Design Engineer

Operations

• Monitor live data center and GPU infrastructure for performance, capacity, and reliability issues
• Lead or support incident response for production issues and drive fast, effective resolution
• Collaborate with engineering teams to ensure monitoring, alerting, and operational tooling are first class and catch issues before customer impact
• Partner with hardware, networking, orchestration, and infrastructure teams to resolve root causes and prevent recurrence
• Contribute to runbooks, on-call practices, and operational maturity as the platform scales
• Help build and scale the Operations & Maintenance function supporting mission-critical AI infrastructure
• Support post-launch monitoring, incident response, reliability, and exceptional customer support

Principal Data Center Operations and Maintenance Engineer

• Profile, analyze, and optimize GPU kernels to improve latency, throughput, and overall utilization
• Identify and eliminate data-plane bottlenecks affecting GPU performance across large-scale AI workloads
• Tune performance-critical workloads across training and inference environments
• Collaborate with AI infrastructure, machine learning, and platform engineering teams to understand workload characteristics and optimize system behavior
• Develop benchmarking methodologies and performance measurement practices across GPU infrastructure
• Evaluate emerging GPU technologies, performance tools, and optimization techniques as hardware platforms evolve
• Contribute to engineering practices that improve GPU efficiency, scalability, and reliability across the fleet

Senior GPU Performance, Kernel Engineer

• Monitor live data center and GPU infrastructure for performance, capacity, and reliability issues
• Lead or support incident response for production issues and drive fast, effective resolution
• Collaborate with engineering teams to ensure monitoring, alerting, and operational tooling are first class and identify issues before customer impact
• Partner with hardware, networking, orchestration, and infrastructure teams to resolve root causes and prevent recurrence
• Contribute to runbooks, on-call practices, and operational maturity as the platform scales
• Help build and scale the Operations & Maintenance function supporting mission-critical AI infrastructure
• Support post-launch monitoring, incident response, reliability, and exceptional customer support
• Build operations and maintenance processes, tooling, systems, and operational discipline for next-generation AI infrastructure

Staff Principal Data Center Operations and Maintenance Engineer

Full-stack Engineer

• Design, develop, configure, and automate AI infrastructure clusters
• Develop infrastructure-as-code, automation, and provisioning systems for compute, networking, and storage
• Deploy and optimize Kubernetes, container, and distributed computing platforms
• Optimize GPU, networking, storage, and system performance for large-scale AI workloads
• Troubleshoot complex issues across hardware, operating systems, networking, storage, and software stacks
• Build reliability, observability, and operational excellence practices for mission-critical infrastructure
• Support platform operation from server and rack installation through software deployment, networking, configuration, cluster bring-up, and automation
• Help shape architecture, tooling, and culture as an early engineering team member

Senior Data Center Infrastructure Software Engineer

• Build and operate production-grade model-serving and inference systems for high-throughput, low-latency AI workloads
• Optimize inference infrastructure for token throughput, latency, scalability, and cost efficiency across model architectures and workloads
• Design systems that maximize GPU utilization while maintaining predictable performance and reliability
• Improve inference-platform scalability and operational maturity as customer demand grows
• Partner with AI training, GPU performance, orchestration, and infrastructure teams to transition models from development to production serving
• Develop monitoring, alerting, and operational practices for reliable inference services
• Investigate and resolve performance, reliability, and capacity challenges across inference workloads
• Contribute to architecture decisions and engineering standards as the platform evolves

Staff Inference Engineer

• Profile, analyze, and optimize GPU kernels to improve latency, throughput, and overall utilization
• Identify and eliminate data-plane bottlenecks affecting GPU performance across large-scale AI workloads
• Tune performance-critical workloads across training and inference environments
• Collaborate with AI infrastructure, machine learning, and platform engineering teams to understand workload characteristics and optimize system behavior
• Develop benchmarking methodologies and performance measurement practices across GPU infrastructure
• Evaluate emerging GPU technologies, performance tools, and optimization techniques as hardware platforms evolve
• Contribute to engineering practices that improve GPU efficiency, scalability, and reliability across the fleet
• Optimize the performance layer powering next-generation AI infrastructure

Staff GPU Performance, Kernel Engineer

• Design, build, automate, and operate data center networking supporting large-scale GPU clusters, AI workloads, and multi-tenant cloud services
• Develop secure tenant isolation architectures across customer environments
• Design resilient, highly available network architectures across compute, storage, and infrastructure services
• Work across multiple networking platforms and vendors, including Arista, Juniper, HPE, and other enterprise networking providers
• Partner with hardware, infrastructure, virtualization, and platform engineering teams to optimize networking for AI training and inference workloads
• Build infrastructure automation for deployment, configuration management, monitoring, and operational excellence
• Define networking standards, architecture documentation, and engineering best practices
• Evaluate emerging networking technologies and shape the long-term evolution of the AI networking platform

Principal Staff Data Center Network Engineer

• Lead the design, development, configuration, and automation of AI infrastructure clusters
• Develop infrastructure-as-code, automation, and provisioning systems for compute, networking, and storage
• Deploy and optimize Kubernetes, container, and distributed computing platforms
• Optimize GPU, networking, storage, and system performance for large-scale AI workloads
• Troubleshoot complex issues across hardware, operating systems, networking, storage, and software stacks
• Build reliability, observability, and operational excellence practices for mission-critical infrastructure
• Support the platform from server and rack installation through software deployment, networking, configuration, cluster bring-up, and automation
• Help make the platform operational and ready for customer workloads
• Shape architecture, tooling, and culture as an early engineer on the team

Staff Data Center Infrastructure Software Engineer

Researcher

• Hold the company's view of where AI networking is going
• Track vendor and hyperscaler roadmaps, research, standards work, and the startup and venture landscape across networking fabrics, topology, optics, collectives, software, operations, telemetry, and tenant-facing capability
• Formulate and validate product and engineering theses using measured cluster performance, isolation requirements, and cost per port
• Own the company's fabric position, including scale-up/scale-out boundaries, transport choices, telemetry, and observability
• Help finance formulate cost-per-port, optics, cabling, reach, power, failure-rate, and substantiated performance analyses
• Own tenant-facing network capability for GPU-as-a-service, including multi-tenant isolation, storage traffic, and contractual commitments
• Support sales and delivery in demanding customer conversations about cluster performance
• Feed product and go-to-market teams with offerings, performance, and pricing information
• Provide technical diligence on network vendors, partners, and prospective tuck-in targets
• Collaborate with the Data Center Expert where the fabric meets the physical plant

Senior AI Training Infrastructure Engineer

Senior Virtualization & Orchestration Engineer

Staff Data Center Network Engineer

Senior Virtualization & Orchestration Engineer

Staff AI Training Infrastructure Engineer

Principal Hardware Design Engineer

Principal Data Center Operations and Maintenance Engineer

Senior GPU Performance, Kernel Engineer

Staff Principal Data Center Operations and Maintenance Engineer

Senior Data Center Infrastructure Software Engineer

Staff Inference Engineer

Staff GPU Performance, Kernel Engineer

Principal Staff Data Center Network Engineer

Staff Data Center Infrastructure Software Engineer

Applied Researcher – Network Expert