Top Tech Jobs & Startup Jobs

Design and evaluate micro-architecture features for an AI accelerator. Build and validate performance and power models, perform PPA trade-offs, profile workloads, identify bottlenecks and HW acceleration opportunities, and collaborate with software teams for cluster-level application modeling and benchmarking.

Lead testing strategy and execution for ML API features, building scalable test frameworks and automation to validate accuracy, fairness, and performance of inference systems. Drive pre-deployment and production validation across distributed cloud, multi-region, and hardware-backed inference environments. Mentor SDETs, champion best practices, debug complex distributed issues, and lead cross-functional quality initiatives to improve test coverage and engineering efficiency.

Design, deploy, and maintain Linux servers and high-speed LAN/WAN networking across manufacturing sites. Implement automation (Ansible, Terraform), manage Palo Alto security, core switching (Arista/Juniper/Cisco), Wi‑Fi and ruggedized hardware, ensure redundancy/QoS, monitor performance, provide Tier 2/3 support and on‑call response, and lead IT/OT modernization and documentation efforts.

New graduate software engineer working on low-level, performance-oriented software for AI accelerator systems. Design, implement, test, and debug hardware-near components, support system bring-up, performance optimization, tooling, and cross-functional collaboration with hardware, firmware, compiler, and infrastructure teams.

Design, deploy, and maintain global WAN connectivity across leased lines and dark fiber. Collaborate with carriers, configure and troubleshoot routing and security (BGP, IPSec, MACsec, VXLAN/EVPN), implement redundancy/QoS, monitor performance, support cloud connectivity (AWS/Azure/GCP), provide Tier 3 incident response, and automate network configuration with Python, Ansible, or Terraform.

Entry-level engineer working at the software/hardware interface: kernel development, ASIC performance modeling, system bringup, performance profiling and debugging. Learn Cerebras Wafer Scale Engine end-to-end, model and tune performance, and validate designs on hardware and simulators using C++ and Python.

Drive end-to-end ML inference performance by building kernel- and system-level performance models, optimizing kernel microcode and compiler algorithms, debugging runtime and cluster performance, and developing tools to visualize performance data from the Wafer Scale Engine and compute cluster.

Design, build, and run automated test frameworks and validation for cloud-based AI/ML inference services. Execute functional, integration, regression, performance, API, and data validation tests using Python/Java, Selenium, SQL, JMeter, and CI/CD tools. Triage defects, perform root-cause analysis, monitor system performance, and document results while collaborating with cross-functional teams to ensure reliable, scalable SaaS deployments.

Operate and scale production AI inference infrastructure, run releases and capacity changes, build self-service CD pipelines and automation, extend telemetry and observability, collaborate on SLOs, post-mortems, and capacity planning to reduce operational toil.

Execute hardware bring-up, validation, and telemetry monitoring for Cerebras AI clusters in data centers. Perform power-on sequencing, first-line troubleshooting, log collection, incident support under senior guidance, and contribute feedback to tooling and documentation while learning system architecture and networking fundamentals.