This role involves designing, implementing, and securing enterprise network services across on-premises, cloud, and hybrid environments. Responsibilities include leading network security engineering, driving automation, and supporting secure browser-based access solutions.
Design and maintain large-scale HPC/AI clusters with a focus on system automation, performance tuning, and infrastructure scalability. Develop CI/CD pipelines and monitoring solutions while collaborating with researchers and engineers to optimize workflows for accelerated computing. Support R&D through proof-of-concepts and contribute to standard methodologies for deploying GPU-accelerated systems.
Design, deploy, and maintain large-scale HPC and AI clusters with a focus on accelerated computing and GPU workloads. Develop automation tooling for infrastructure deployment, monitoring, and self-service resource consumption. Troubleshoot across bare metal, OS, software stack, and application layers while supporting R&D through proof-of-concept initiatives and performance optimization.
Design and maintain large-scale HPC/AI clusters with a focus on system automation, monitoring, and performance tuning across bare metal, OS, and application layers. Develop CI/CD pipelines and self-service tooling for infrastructure management, supporting cutting-edge AI and GPU computing workflows. Collaborate with researchers and engineers to deploy and optimize accelerated computing platforms at scale.
This role involves designing, implementing, and maintaining large-scale HPC/AI clusters, managing job/workload schedules, and developing CI/CD pipelines. You will work closely with HPC, OS, GPU compute, and systems specialists to architect and optimize performance platforms, and support R&D activities.
This role involves leading the strategic design, development, and operational oversight of critical research platforms, including HPC, HTC, cloud services, and large-scale research storage systems. The Principal Research Infrastructure Engineer and Team Lead will manage a small team, ensuring robust and scalable infrastructure aligned with institutional and national research priorities.
This role involves leading the design and implementation of AI-driven risk decisioning systems, focusing on fraud detection, credit risk, and financial crime prevention. The Principal Engineer will translate regulatory and risk requirements into scalable, cloud-native technical solutions using multi-agent AI, real-time decision engines, and event-driven architectures. They will mentor engineering teams, drive innovation in highly regulated environments, and collaborate with stakeholders across product, compliance, and data to deliver resilient, enterprise-grade platforms.
Design and optimize high-performance AI inference systems for large-scale models using cutting-edge NVIDIA GPU hardware. Develop and enhance inference frameworks like vLLM, implement compiler and kernel optimizations, and lead benchmarking efforts across multi-GPU and multi-cloud environments. Collaborate with research and systems teams to integrate novel techniques such as speculative decoding and prefill-decode disaggregation into production-grade software.
This role involves developing and optimizing high-performance AI inference systems, focusing on GPU kernel optimization, compiler infrastructure, and large-scale model deployment. You'll contribute to frameworks like vLLM, enhance inference efficiency using techniques such as speculative decoding and tensor parallelism, and build benchmarking tools for industry standards like MLPerf. The position emphasizes research, performance engineering, and collaboration across compiler, scheduling, and systems teams to advance AI inference at scale.
Develop and optimize high-performance AI inference systems for large-scale models on NVIDIA GPUs. Work on inference frameworks like vLLM, optimize GPU kernels and compilers, design benchmarking methodologies, and contribute to MLPerf submissions. Collaborate across teams to improve performance in multi-GPU, multi-node, and cloud environments, with opportunities to conduct and publish research in ML systems.
Design and optimize high-performance AI inference systems for large-scale models using NVIDIA's latest GPU hardware. Develop and benchmark GPU kernels, improve inference frameworks like vLLM, and contribute to compiler infrastructure and distributed scheduling. Work at the intersection of systems, performance, and machine learning to push the boundaries of accelerated computing.
This role involves designing and deploying AI agents that automate penetration testing by planning, using security tools, and reasoning over findings. The engineer will build sandboxed cyber-lab environments using containers and Kubernetes, integrate LLMs with security tooling, and optimize agent performance through prompt engineering and cost efficiency. The position emphasizes collaboration with security researchers to translate offensive techniques into AI-driven behaviors within a scalable, polyglot production environment.
This role involves hands-on management of cloud infrastructure, automation, and deployment pipelines, with a focus on building reliable, secure, and scalable production environments on Azure. The engineer will collaborate closely with software teams, using Terraform, Kubernetes, Docker, and CI/CD tooling to support applications in the chemical and process industries. Emphasis is placed on operational ownership, troubleshooting, and solving complex infrastructure challenges in a technical domain.
This role involves maintaining secure, high-performing services within a critical infrastructure environment, focusing on automation, problem-solving, and agile delivery. You'll work with Kubernetes/OpenShift in on-prem production environments, manage IaC via Terraform and CI/CD pipelines, and implement observability using tools like ELK, Splunk, and Grafana. The position supports mission-critical systems, often in air-gapped or highly secure settings.
This role involves building and maintaining the infrastructure that supports AI deployment, focusing on data pipelines, system integrations, and production-grade reliability. The engineer will take AI prototypes from concept to scalable, robust systems integrated with CRMs, ERPs, and SaaS platforms. Emphasis is on automation, data integrity, and seamless communication between AI agents and enterprise systems.
