Jobs at Designworks Talent LLC

Designs and operates virtualization and Kubernetes orchestration platforms for large-scale GPU and HPC workloads. Responsibilities include GPU cluster provisioning, workload scheduling, resource allocation, multi-tenant infrastructure, cluster lifecycle management, automation, reliability, security, and scalability. The role partners with hardware, networking, infrastructure, and AI platform teams while owning complex systems from architecture through production operations.

Build and scale distributed infrastructure for large-scale AI model training across GPU clusters. Responsibilities include improving reliability, fault tolerance, checkpointing, recovery, resource utilization, training pipelines, developer tooling, and operational processes. The role partners with platform, orchestration, performance, and machine learning teams to diagnose training issues and support production-ready AI workloads.

Optimize GPU kernels and data-plane performance for large-scale AI training and inference. Profile workloads, identify bottlenecks, improve latency, throughput, and utilization, develop benchmarking practices, and evaluate GPU technologies across distributed infrastructure. Collaborate with AI infrastructure, machine learning, and platform engineering teams to improve GPU efficiency, scalability, and reliability.

Build and scale distributed infrastructure for large-scale AI model training across GPU clusters. Responsibilities include improving reliability, fault tolerance, checkpointing, recovery, throughput, resource utilization, and cost efficiency. The role integrates models into production training pipelines, develops automation for AI researchers, diagnoses distributed training issues, and establishes platform reliability practices. Candidates should have experience with distributed training systems, foundation models, multi-node GPU workloads, complex distributed systems, and ML infrastructure at scale.

Lead the architecture, mechanical design, configuration, validation, and production deployment of GPU servers and rack-scale AI infrastructure. Partner with NVIDIA, AMD, ODMs, OEMs, data center engineering, networking, and operations teams. Own rack layouts, power distribution, cooling, airflow, cable management, serviceability, qualification, and hardware standards while balancing performance, reliability, manufacturability, scalability, and cost.

Optimize GPU kernels and data-plane performance for large-scale AI training and inference workloads. Profile bottlenecks, improve utilization, latency, throughput, and scalability, develop benchmarking practices, evaluate GPU technologies, and collaborate with infrastructure, machine learning, and platform engineering teams.

Build and operate production-grade AI model-serving and inference systems. Optimize large-model workloads for throughput, latency, GPU utilization, scalability, reliability, and cost. Collaborate with training, GPU performance, orchestration, and infrastructure teams, while developing monitoring, alerting, and operational practices. Investigate performance and capacity issues and contribute to platform architecture and engineering standards.

Design, build, and operate virtualization and Kubernetes orchestration platforms for large-scale GPU and HPC workloads. Develop automated provisioning, scheduling, resource allocation, multi-tenant capacity management, and cluster lifecycle systems. Improve platform reliability, security, scalability, and operational maturity while partnering with hardware, networking, infrastructure, and AI platform teams. Own complex systems from architecture through production operation and contribute to engineering standards and platform strategy.

Conduct applied research on AI models, inference systems, accelerator ecosystems, and data center infrastructure. Evaluate emerging technologies and translate findings into engineering, product, infrastructure, financial, and commercial strategies. Own technical views on GPU hall capacity, power, cooling, density, cost, and performance. Support customer and partner discussions, infrastructure diligence, benchmarking, and technology investment decisions. The role requires hands-on inference, compiler, kernel, runtime, and multi-accelerator experience, with up to 25% international travel.

Evaluate and shape networking strategy for large-scale AI infrastructure. Track networking technologies, vendors, standards, and research; define scale-up and scale-out fabric positions; validate performance, cost, isolation, and observability requirements; and guide build-versus-buy decisions. Diagnose production collective communication issues, assess GPU networking and tenant-facing capabilities, support finance, sales, delivery, and diligence activities, and translate applied research into engineering and commercial decisions.

Develop and maintain the company’s technical perspective on AI data center infrastructure, including power, cooling, density, rack architecture, siting, and economics. Evaluate emerging technologies, vendors, sites, and infrastructure partners; build defensible product and engineering recommendations; own technical reference views for GPU halls; develop cost models; and influence finance, sales, product, engineering, and investment decisions. The role is a senior individual contributor requiring hands-on experience with large-scale data centers, GPU environments, power procurement, cooling, and infrastructure economics.

Leads large-scale AI infrastructure and data center deployment programs from planning through commissioning and production handoff. Coordinates engineering, facilities, supply chain, vendors, and contractors across GPU infrastructure, networking, storage, power, cooling, and site readiness. Owns schedules, dependencies, risks, technical acceptance, readiness, executive reporting, and operational handoff. Requires strong technical credibility, deployment experience, vendor accountability, and readiness to travel up to 50%.

Lead the engineering organization responsible for bringing data center hardware into production-ready AI and HPC infrastructure. Own automated provisioning, Linux deployment, configuration, validation, GPU clusters, Kubernetes environments, monitoring, and workload readiness. Establish standards across servers, GPUs, networking, storage, firmware, and software automation while partnering with hardware, network, SRE, data center operations, and software teams. Build and develop a high-performing engineering organization capable of scaling infrastructure across thousands of servers or GPUs.

The CIO will build and lead the enterprise technology function for a rapidly scaling AI company. Responsibilities include enterprise technology strategy, architecture, ERP and business systems, AI-enabled automation, IT operations, governance, controls, vendor management, budgeting, and cost optimization. The leader will recruit and develop the technology organization, oversee implementation partners, establish scalable systems and standards, and partner with executive leadership to align technology investments with business growth and public-company readiness.

The IT Systems Engineer will manage Windows Server environments, VMware, Citrix infrastructure, and Ivanti Endpoint Management, while providing technical support and implementing security practices in an enterprise environment.

The IT Consultant engages with clients to understand requirements, provides strategic guidance, implements tech solutions, and supports IT initiatives while fostering strong relationships.

Lead design of GPU servers and rack-scale infrastructure for AI workloads, partnering with GPU vendors, ODMs/OEMs, and data center teams. Own mechanical layout, power distribution, thermal design, cable management, validation, and production readiness while balancing performance, reliability, serviceability, and cost. Influence hardware standards and strategy across large-scale deployments.

Build and scale distributed training infrastructure for large AI models across GPU clusters. Improve training reliability, efficiency, fault tolerance, checkpointing, and resource utilization. Integrate training systems with production ML pipelines, develop tooling and automation to improve developer experience, diagnose training throughput and stability issues, and establish operational best practices for a growing AI infrastructure platform.

Design, build, and operate GPU-focused virtualization and Kubernetes orchestration systems for multi-tenant AI/HPC clusters. Implement automated provisioning, workload scheduling, resource management, and tooling to scale, secure, and run GPU compute reliably across production environments while partnering with hardware, networking, and platform teams.

Optimize GPU kernels and data-plane performance for large-scale AI workloads by profiling, identifying bottlenecks, tuning kernels, developing benchmarks and tooling, and collaborating with ML and platform teams to maximize GPU utilization, throughput, and reliability.