Top Tech Jobs & Startup Jobs

Lead block-level and top-level physical design for advanced AI semiconductor chips from netlist through foundry tapeout. Responsibilities include floorplanning, placement, clock tree synthesis, routing, timing closure, PPA optimization, power-grid verification, EM/IR resolution, flow automation using Tcl/Python, and collaboration with physical verification and foundry teams.

Own end-to-end embedded software program execution across firmware, drivers, bootloaders, hardware, architecture, compiler, runtime, and validation teams. Align software milestones with tape-out, silicon and board bring-up, validation, and customer delivery. Identify and resolve cross-team risks, manage schedules and operating cadence, support platform enablement, and coordinate distributed engineering teams and external partners for AI accelerator deployments.

Leads customer and partner solution development for AI acceleration platforms, including system design, benchmarking, performance tuning, proof-of-concepts, deployment, technical pre-sales, reference applications, partner enablement, and ecosystem collaboration. Influences product roadmaps while integrating AI hardware, software, systems, and customer workloads for edge, datacenter, and embedded applications.

Lead block-level physical implementation from synthesis and floorplan through CTS, routing, and sign-off. Optimize PPA via deep timing, power, and congestion analysis, automate flows with Tcl/Python, resolve DRC/LVS/IR/timing issues, and collaborate with RTL/DFT teams to left-shift constraints and ensure clean GDSII tape-outs.

Lead full RTL-to-GDSII physical design for sub-5nm SoCs: own full flow, optimize PPA, build scalable push-button methodology, remove execution bottlenecks, and collaborate with RTL/Architecture/DFT/foundry/EDA teams.

Conduct hardware-aware ML research and engineering to accelerate AI inference. Build fine-tuning and post-training pipelines, benchmarking and profiling tools, and collaborate with hardware, compiler, and quantization teams to optimize model quality, latency, throughput, and power on novel silicon.

Lead and define end-to-end DFT architecture for complex SoCs, including scan insertion, ATPG, MBIST, IJTAG and boundary scan. Drive edge-specific IST/POST strategies, collaborate across design, PD, and yield teams, and lead post-silicon bring-up and ATE debug to ensure manufacturability and reliability.

Lead development and ownership of full-chip STA sign-off methodology and timing convergence strategy. Architect and deploy STA/ECO flows, benchmark EDA tools (Cadence Tempus), build automation (Tcl/Python/Perl), define MMMC and margin strategies, and collaborate with RTL, CAD, DFT, and Physical Design to meet PPA and tape-out goals.

The Staff DFT Engineer at EnCharge AI will define and implement the DFT architecture for AI accelerators, manage DFT constraints, generate test patterns, and optimize for Edge AI applications while collaborating with ATE teams for silicon debugging.

Develop and optimize AI models for efficient inference on custom silicon. Build fine-tuning pipelines, implement acceleration techniques (quantization, sparsity, distillation), create benchmarking and profiling tools, and collaborate with hardware, compiler, and quantization teams to translate algorithmic improvements into real hardware gains.