Top Tech Jobs & Startup Jobs

Design and optimize hyperscale data pipelines and ETL for multi-terabyte hardware telemetry. Build ML-driven failure prediction and anomaly detection, create dashboards and real-time telemetry visualizations, lead hardware characterization and A/B studies, perform root cause analysis, and collaborate with cross-functional teams to improve hardware performance, reliability, and energy efficiency for AI platforms.

Perform post-silicon validation and debug of wafer-scale engines; characterize high-speed serial interfaces; support wafer bring-up, burn-in, and manufacturing operations; develop hardware, automated regression tests, and Python/Bash debug tools; collaborate with hardware and system software teams and document issues and flows.

Lead product strategy and execution for strategic AI customers: embed with lighthouse accounts, design PoCs showcasing ultra-low-latency inference, advise on model selection and long-term AI strategy, drive adoption and expansion, and translate customer insights into roadmap priorities across product, engineering, and GTM teams.

Build and maintain the inference orchestration layer across datacenter clusters. Design and implement highly available, low-latency systems, define platform direction (Kubernetes CRDs/operators), lead production incident response, optimize performance and capacity, drive observability and security, and collaborate with ML, product, and cloud teams to productionize scalable inference services.

Develop and implement verification strategies and reusable testbenches (SystemVerilog/UVM/DPI). Create tests, run regressions, collect coverage, debug across simulation, emulation, and silicon bring-up, and collaborate with architecture, RTL, firmware, and validation teams to improve verification infrastructure and methodology.

Own and architect the Inference Cloud Platform: design multi-region topology, service discovery, routing, load balancing, caching, batching, and traffic management. Improve availability, latency, throughput, and resilience at high QPS. Lead production incident response, observability, capacity planning, and mentor senior engineers while making high-consequence architectural decisions and delivering production code.

Build and maintain automation for bare-metal cluster deployments: provisioning, configuration, validation, and operational handoff. Convert manual steps into tested pushbutton workflows, troubleshoot Linux, networking, storage, and Kubernetes issues, and add observability and health checks to improve deployment reliability.

Design, build, and operate CI/CD, Kubernetes-based platforms, deployment automation, and observability for engineering workflows. Improve reliability, performance, and scalability across cloud and on-prem environments, debug cross-boundary failures, perform root-cause analysis, and deliver durable platform software and self-service tooling.

Design, implement, and maintain automated assembly and test systems. Program and troubleshoot PLCs, HMIs, SCADA, robotics, and industrial controls. Improve productivity and quality, lead automation projects, perform root-cause analysis, create control documentation, train staff, and support preventive maintenance and safety compliance.

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.