Senior AI-Driven ASIC Implementation Lead

OHO Group (Lithuania); San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Stealth Startup in San Francisco is seeking an engineering leader to drive physical implementation for custom ASICs. You will set up architecture, collaborate with a broad team, and eventually build out your own implementation flows.

The role requires hands-on use of AI-enabled design tools, reinforcement-learning-based optimisation, and expert Python/Tcl skills for automation and backend improvement. You will own end-to-end physical design challenges and drive PPA in high-performance AI

Qualifications

• 5–10 years of physical-design experience with a track record of tape-outs for large-die SoCs or compute subsystems.
• Hands-on experience integrating AI-enabled design-optimisation tools into production flows.
• Strong understanding of reinforcement-learning principles and their application to EDA and layout density.
• Expert-level Python and Tcl for automating custom infrastructure and layout-iteration flows.

Responsibilities

• Deliver physical implementation for high-throughput, low-latency accelerators, optimised for KPIs.
• Define and drive layout strategy for IP, high-bandwidth interfaces and large on-chip SRAM.
• Lead deployment of AI-driven design-optimisation engines to automate macro placement and clock/power distribution.
• Address physical-design challenges for AI workloads, including IR-drop, thermal-density and GHz clock-tree synthesis.
• Apply reinforcement-learning principles across backend implementation stages to drive design optimisation.

Skills

Physical design

Python

Tcl

AI-enabled design tools

STA/DRC/LVS

Reinforcement learning

Tape-out experience

Automation

Tools

EDA tools

Job description

Stealth Startup in San Francisco is seeking an engineering leader to drive physical implementation for custom ASICs. You will set up architecture, collaborate with a broad team, and eventually build out your own implementation flows.

The role requires hands-on use of AI-enabled design tools, reinforcement-learning-based optimisation, and expert Python/Tcl skills for automation and backend improvement. You will own end-to-end physical design challenges and drive PPA in high-performance AI