Top Tech Jobs & Startup Jobs in Austin, TX

Own and optimize IBM Spectrum LSF scheduling across 15–25 federated cells. Diagnose scheduler latency, MultiCluster forwarding and cross-cluster contention, design scalable cell topology, and encode scheduler policy through infrastructure as code. Partner with CAD and methodology teams on large-scale EDA workloads, license constraints, and tape-out bursts. Requires deep LSF internals expertise, production MultiCluster experience, strong Linux fundamentals, and Python, Perl, and shell scripting skills.

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.

Owns multi-generation system power and performance architecture for datacenter and high-performance platforms. Defines HSIO power management, DVFS, P-state, low-power, boot, initialization, reset, and recovery strategies across silicon, firmware, ASIC, board, and platform teams. Drives rack-scale power telemetry and platform coordination, diagnoses system-level performance and power issues, and influences roadmap and product decisions through silicon correlation and release. The role requires deep technical ownership, cross-functional authority, and experience delivering architectures across multiple silicon generations.

Design and develop RTOS and hypervisor features, bring up and optimize software stacks on NVIDIA Tegra SoCs, and debug system-level issues across hardware, firmware, RTOS, and virtualization layers. The role develops high-integrity automotive software using safety and security practices, while leading cross-team technical initiatives and coordinating execution. Candidates need strong C/C++ systems programming, embedded software expertise, operating-system knowledge, hardware-assisted virtualization experience, and at least eight years of experience.

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.

Own production Slurm support cases for AI and HPC customers, diagnosing scheduling, resource allocation, accounting, authentication, networking, storage, database, GPU, and high-availability issues. Advise on configuration, upgrades, operations, and incident recovery; investigate performance and scalability problems; collaborate with engineering on defect reports and reproductions; and develop technical guides, diagnostic tools, and training. The role requires extensive production Slurm administration experience and expert Linux systems knowledge.

Design and implement GPU compiler components, optimize compiler performance, solve graphics and parallel-computing compilation problems, collaborate with GPU driver and architecture teams, contribute to LLVM and DXC open-source projects, and help define SPIR-V extensions and standards.

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.