Team Description: 

The Infrastructure Team builds the foundation that enables the company to operate safely, robustly, and move at light-speed. We run a mixture of cloud-based and on-prem systems and have a user base spanning from highly technically proficient engineers to non-technical scientists and doctors; but all of them need solid systems, rugged networking, and bullet-proof software to do their jobs. 

Job Responsibilities and Description: 

As a Software Engineer, ASIC, you will integrate tightly with teams across the company, and span all layers of the work environment stack, from deployment of physical hardware on the manufacturing line, to custom tooling to stream neural recordings from implants. You will: 

• Build and maintain scalable EDA compute infrastructure for analog and digital chip design 
• Develop distributed simulation clusters to accelerate circuit verification at scale
• Manage and optimize license infrastructure across multiple EDA vendors
• Architect high-performance and robust shared storage for terabyte-scale design data
• Develop build-system infrastructure and tooling for firmware targeting custom ASICs
• Automate bare-metal server provisioning and lifecycle management for EDA compute fleets
• Enhance developer experience for IC design and firmware engineers through monitoring, self-service tooling, and robust automation

Tech Stack:

• Rust, Python, Go
• Cadence Virtuoso, Cadence Spectre
• Terraform, Ansible, Packer, MAAS
• Bazel and Nix
• Prometheus, ClickHouse, Grafana
• Ray, Docker, Kubernetes
• AWS, OCI (Oracle Cloud Infrastructure)
• RHEL, Ubuntu

Required Qualifications:

• Experience administering EDA compute environments and toolchains (Cadence, Synopsys, Siemens/Mentor)
• Working knowledge of FlexLM license server administration and capacity management.
• Working knowledge of compiled languages, ideally Rust, Go, or C/C++.
• Experience with high-performance Linux storage: NFS, ZFS, Ceph, iSCSI, or similar.
• Systems administration experience on RHEL and Ubuntu, kernel tuning for compute workloads
• Familiarity with distributed compute frameworks (Ray, Slurm, LSF, or similar)
• Experience with bare-metal provisioning and lifecycle management at datacenter scale
• Familiarity with embedded cross-compilation toolchains and build systems (ARM, Bazel, Nix)
• Experience with multi-cloud deployments and hybrid on-prem/cloud architectures
• Bachelor's degree in computer science, a related field, or an equivalent combination of education, training, and/or experience