Top Tech Jobs & Startup Jobs in Austin, TX

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.

Develop and deploy internal ASIC design tools, RTL generation workflows, verification utilities, and methodologies across NVIDIA engineering teams. Maintain DevOps-style automated RTL generation operations, support reusable design blocks, and integrate workflows into IP development environments. Requires digital design and Verilog expertise, Python or Perl scripting, Unix/Linux shell scripting, and Makefiles. Experience with ASIC verification, clocks/resets, CDC flows, synthesis, and timing is advantageous.

Architect and maintain profiling frameworks for CPU and GPU environments, benchmark EDA applications, analyze memory and kernel performance, identify bottlenecks, optimize memory usage, and develop predictive models for hardware procurement and cloud instance selection.

The Senior LLVM Compiler Engineer will contribute to upstream LLVM development, lead design discussions, and ensure high-quality compiler solutions in collaboration with open-source communities.

Design, build, and operate global, multi-cloud HPC service platforms. Own IaC-driven provisioning, reliability, observability, capacity planning, incident response, and automation to ensure high uptime and QoS for internal customers.

Develop and implement core firmware features for Mellanox NICs supporting cloud, HPC, and storage networking. Collaborate with architecture and software teams, create verification tests, debug complex hardware/software systems, and contribute to firmware maintenance, qualification, and releases. The role requires strong C/C++ development, networking knowledge, PCIe and virtualization experience, scripting skills, and familiarity with distributed, high-performance computing environments.

Develop innovative physical design methodologies for GPUs and SOCs, focusing on PPA improvements and collaboration across multiple teams. Use AI/ML to enhance design efficiency.

Develop platform software using container runtimes and Kubernetes to enable GPU-accelerated applications. Build GPU resource support for container orchestrators, integrate software across NVIDIA’s technology stack, and contribute to cloud-native systems. The role requires systems programming, Linux internals, distributed systems, cloud computing, and performance, security, and reliability expertise. Responsibilities include solving complex problems, managing multiple priorities, and collaborating across engineering teams.

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 Rust-to-GPU compiler pipelines, custom intermediate representations, optimizers, and ahead-of-time, just-in-time, and link-time compilation tooling for NVIDIA GPUs. Design Rust-based safety abstractions across GPU execution boundaries and expose low-level CUDA hardware features. Responsibilities include compiler architecture, GPU code generation, debugging, profiling, benchmarking, and independently driving complex engineering initiatives from research through production.