Top Tech Jobs & Startup Jobs

Support cost accounting and fixed asset processes for a manufacturing hardware company: execute inventory reconciliations, capitalize costs, manage fixed asset lifecycle, analyze COGS and manufacturing variances, prepare audit schedules, and partner with Supply Chain and Procurement.

Design and develop software to automate bare-metal configuration, orchestration, scheduling, and job placement for large wafer-scale clusters. Build upgrade/patch workflows, monitoring, HA failure handling, visualization and alerting, and user/admin tools. Ensure on-premise and cloud deployment support and develop tests and debugging tools for distributed systems.

Lead automation and platform engineering to eliminate toil and deliver self-service GitOps-driven CD, capacity provisioning, and observability for large-scale inference clusters. Define SLOs/SLIs, mentor SREs, support incident escalation, implement reliability practices, and measure impact via deployment velocity, SLO compliance, MTTR, and adoption of self-service workflows.

Drive end-to-end ML model inference performance: build kernel- and system-level performance models, optimize kernel microcode and compiler algorithms, debug runtime performance on system and cluster, and develop tooling to visualize and analyze performance data from the Wafer Scale Engine and compute cluster.

Characterize and optimize performance of state-of-the-art AI models on Cerebras hardware. Build kernel-level and end-to-end performance models, debug kernel microcode and compiler algorithms, analyze runtime and cluster performance, and develop tools to visualize performance data for the Wafer Scale Engine and compute cluster.

Lead design, development, and operation of a globally distributed inference orchestration platform. Drive architecture, reliability, performance, observability, security, and CI/CD. Own critical production systems, mentor senior engineers, resolve high-impact incidents, and collaborate with ML, Product, Infrastructure, and Cloud teams to deliver scalable, low-latency inference services.

Design and analyze 3D integrated ASIC/SoC products, including block and full-chip physical design, verification, packaging, power, clock, and cooling analysis. Collaborate with architecture and RTL teams to optimize power/performance/area, resolve DRC/LVS and IR/EM issues, enhance flows via scripting, and perform R&D on novel 3D integration concepts and full-chip integration.

Own quality and reliability of the Cerebras Inference Platform by building test infrastructure and automation, validating Kubernetes and hardware deployments, debugging distributed systems and networking, developing testbeds for performance and scalability, and partnering with platform engineers to ensure production-ready releases.

Architect and drive a unified, self-service inference control plane and reliability practices for large-scale multi-datacenter and cloud inference fleets. Build capacity orchestration, rollout safety, observability, SLO-based reliability, incident response, and automation; mentor senior SREs and measure impact through reduced toil, faster deployments, and SLO compliance.

Build, productionize, and optimize a GPU-based inference stack combining GPU prefill with Cerebras decode. Implement and operate model-serving APIs, vLLM/PyTorch/ROCm runtimes, deployment and reliability practices, performance profiling and optimization, cross-layer debugging, numerical validation, and benchmarking/infrastructure for production inference at scale.