Top Tech Jobs & Startup Jobs in Boston, MA

Leads an engineering team building NVIDIA’s large-scale AI infrastructure platform. Owns roadmap, architecture, reliability, on-call health, Kubernetes-based systems, scheduling, data movement, and fleet-scale automation. Partners with research, infrastructure, cloud, storage, networking, vendors, and open-source communities to turn AI training needs into reliable platform capabilities. Hires and develops senior engineers, drives technical decisions, and improves service reliability through observability, incident response, and postmortems.

Lead development and improvement of ASIC integration, build, simulation, CI, and debug infrastructure for high-speed IO GPU IP. Apply automation, metrics, and AI-assisted workflows to improve reliability, turnaround, and debuggability. Collaborate across design, verification, firmware, methodology, and infrastructure teams to root-cause failures and deliver durable process improvements.

Provide advanced technical support for NVIDIA InfiniBand, NVLink and GPU-based HPC/AI infrastructure. Troubleshoot installations, networking and interoperability issues on multi-distro Linux, reproduce and resolve customer problems, use packet analysis and cluster tools, and collaborate with engineering, marketing, and support teams to improve processes and documentation.

Verify ASIC designs and high-speed I/O controllers at IP, subsystem, and SoC levels using UVM and coverage-driven methodologies. Develop reusable models, monitors, checkers, scoreboards, test plans, tests, and verification infrastructure. Perform random-stimulus, functional-coverage, assertion-based verification and debugging while collaborating with architects, designers, and pre- and post-silicon teams. Relevant protocols include Ethernet, NVLink, USB, SATA, and CXL.

Build and operate AI infrastructure systems, including telemetry pipelines, observability dashboards, incident-management automation, hardware and software catalogs, service ownership data models, and self-service discovery tools. Lead cross-functional infrastructure initiatives and develop AI-assisted operational tooling. The role requires production software and infrastructure expertise, programming experience, and the ability to improve reliability, visibility, incident response, and operational efficiency across NVIDIA GPU cloud environments.

Design, deploy, maintain, tune, and secure enterprise Linux environments supporting high-performance EDA compute farms. Lead infrastructure-as-code automation, configuration management, continuous deployment, and tooling development using Python, Bash, or Go. Manage LDAP/SSSD naming services, NFS and Autofs storage, virtualization, container infrastructure, registries, networking, and infrastructure telemetry. Support highly available, scalable systems and potentially integrate cloud platforms, CI/CD pipelines, EDA toolchains, and HPC clusters.

Develop platform software for SOCs, including drivers, BSP, CI/CD automation, OS and multimedia driver bring-up, debugging, and system-level triage. Collaborate with OEMs and global engineering teams across the full product lifecycle from pre-silicon modeling through production. Contribute to architecture definition, boot, security, reliability, resiliency, and embedded-system software for Windows and Linux platforms.

Design, deploy, operate, and automate reliable large-scale EDA infrastructure services on NVIDIA hardware. Build tools that reduce manual work, support distributed private and public cloud systems, define service-level objectives, improve observability, and participate in incident response and on-call rotations. The role also provides systems-design guidance to peer teams and may involve Kubernetes, OpenStack, Docker, Slurm, multi-cloud infrastructure, networking, storage, and GPU-based systems.

Lead research, architecture, and production development of hardware formal verification tools enhanced by agentic AI. Build formal verification algorithms, multi-agent systems, solver orchestration, RTL analysis, automated SystemVerilog Assertion generation, and high-performance C/C++ implementations for commercial EDA suites. The role requires deep expertise in formal methods, hardware verification, AI frameworks, RTL languages, and 15+ years developing advanced EDA tools.

Design and operate large-scale infrastructure supporting GPU- and CPU-based EDA workloads. Build automation for provisioning, configuration, deployment, lifecycle management, monitoring, health remediation, cluster enrollment, and recovery across Linux compute fleets. Integrate services with schedulers, infrastructure systems, and observability platforms. Participate in incident response, root-cause analysis, capacity planning, and reliability improvements while collaborating with EDA, networking, storage, and hardware teams.