Top Tech Jobs & Startup Jobs

Support system-level bring-up to configure, test, and validate systems before shipment. Collaborate with ASIC, software, diagnostics, and QA teams to automate and streamline workflows. Troubleshoot cross-domain issues, design tracking processes, report bring-up metrics, and implement software automation to scale manufacturing bring-up operations.

Develop and deploy large-scale deep learning models and LLMs for customers on Cerebras hardware. Scope projects, design training recipes (pretraining, SFT, RLHF/DPO), manage data pipelines, scale distributed training, analyze convergence/loss dynamics, build agentic systems, and serve as technical customer lead.

Lead a hands-on engineering team to improve kernel-centric reliability of large AI compute clusters. Own technical vision, build diagnostic and debug tooling, collaborate with SW and HW teams to reduce downtime, speed failure analysis, and mentor engineers to deliver scalable, reliable production systems.

Implement, optimize, and validate high-performance machine learning and linear algebra kernels for the Cerebras Wafer-Scale Engine. Develop low-level kernel routines using the Cerebras Software Language and C++, apply parallel programming techniques, profile and tune performance, debug correctness and hardware utilization issues, build tests and validation, and collaborate with compiler, performance, and hardware engineers.

Lead administration and support for NetSuite and adjacent enterprise systems. Manage user access, troubleshoot production issues, monitor integrations, perform configuration and minor enhancements, support financial and supply chain cycles, own release and change management, drive automation and process improvement, support audits and SOX controls, and partner with business stakeholders for UAT, training, and governance.

Build and improve C++ simulator infrastructure for Cerebras Wafer-Scale Engine, covering functional and pipeline-accurate simulation. Develop tooling, tests, and performance improvements; collaborate with architecture, design verification, compiler/runtime teams; debug cross-domain issues and maintain test/regression coverage and documentation.

Lead design and execution of automated tests for large-scale AI clusters. Define test strategies, automate reliability, performance, stress, failure, and security tests across software (Kubernetes, monitoring) and hardware (wafer-scale accelerators, CPUs, interconnects). Debug distributed systems and datacenter hardware, and champion cluster uptime and observability.

Lead synthesis, placement, routing, and full-chip physical design for high-speed AI silicon. Perform block and full-chip physical verification, timing closure, power/performance/area optimization, DRC/LVS and IR/EM resolution, STA and constraint drafting, floorplanning, and script-based flow enhancements. Collaborate with RTL teams to integrate blocks into the full-chip architecture.

Design and implement embedded and system-level software for the Wafer Scale Engine, including Linux BSP, administrative services, hardware monitoring, debugging, and cross-team collaboration to improve reliability, observability, and operability at datacenter scale.

Build and productionize a GPU-accelerated prefill serving path for LLM inference: deploy, operate, and optimize model-serving, runtime (vLLM/PyTorch/ROCm), multi-node GPU infrastructure, reliability, performance, correctness, and benchmarking.