Top Tech Jobs & Startup Jobs

Lead and execute power, thermal and performance validation for AI compute silicon and systems across pre- and post-silicon stages. Define validation strategies, develop automation and benchmarks (Python), run characterization, analyze large datasets to find bottlenecks, and collaborate with architecture, RTL, firmware and systems teams to drive performance and reliability improvements.

Deliver microarchitecture and RTL for Graphcore silicon: integrate IP/subsystems into SoC, maintain build/config environments, run synthesis/lint/CDC/timing checks, support verification and physical design handoffs, debug integration issues, improve automation and integration flows, and produce high-quality documentation.

Lead architecture and implementation of system management interfaces for AI infrastructure. Translate strategy into technical plans, mentor engineers, improve reliability, automate deployment and CI/CD, collaborate across hardware, firmware and platform teams, and drive production readiness and observability.

Build and maintain infrastructure, tools and services to support AI research and engineering. Deploy and operate services using Kubernetes/Docker, manage cloud infrastructure with Terraform, support CI/CD, and enable ML application deployment and accelerator hardware management.

Build, own, and maintain infrastructure, tools, and services to support ML research and engineering. Deploy and maintain services with Kubernetes and Docker, manage cloud infrastructure (Terraform), and improve CI/CD, build, test, deployment, and productisation of ML software on HPC AI platforms.

Build and maintain infrastructure, tools and services to support AI research and engineering. Deploy and operate services with Kubernetes and Docker, manage cloud infrastructure (Terraform/AWS), support CI/CD, and enable ML application deployment, orchestration, and accelerator hardware management.

Develop and maintain tools for software infrastructure, deploy and manage Kubernetes and Cloud Infrastructure to support AI software and hardware.

Develop tools and services for building, testing, and integrating Machine Learning Software. Collaborate with QA and development teams, focusing on best practices and troubleshooting integration issues.

Design, develop, automate and report integration and system tests across firmware, BMC and Linux device drivers. Build scalable CI/CD pipelines and internal tools, triage/debug cross-layer issues, drive quality and reliability, collaborate with component teams, and mentor junior engineers.

Lead post-silicon debug and validation for AI compute silicon and platforms. Identify, reproduce, analyze, and resolve complex silicon, firmware, software, and system-level issues. Develop automated debug and validation infrastructure, tests, and tooling using Python and related technologies. Collaborate across architecture, RTL, firmware, and systems teams, define validation metrics and debug workflows, and support silicon readiness and release decisions.