# Physical Design Engineer, Hardware at River AI

- Company: River AI
- What the company does: Develop frontier language models and agents. Training, reinforcement learning, and inference in one system, on River Cloud or your own GPU cluster. Backed by General Catalyst.
- Company website: https://river.ai/
- Type: Startups (AI role)
- Level: Mid level
- Location: Palo Alto, CA; Austin, TX
- Work setup: On-site
- Pay: $200K to $420K base salary per year (USD)
- Posted: 2026-05-15
- Apply by: 2026-10-08
- Apply: https://job-boards.greenhouse.io/riverai/jobs/4250010009
- Page: https://www.1752.vc/careers/jobs/river-ai-physical-design-engineer-hardware/

## About the role

We are looking for exceptional physical design engineers to transform our high-performance architectural concepts into production-ready silicon. You will own the physical implementation flow from synthesis through tape-out, pushing the absolute limits of advanced foundry nodes to maximize PPA. You will take ownership of block-level and top-level physical design, collaborating tightly with RTL designers to close timing, electrical, and physical verification for our custom AI accelerator.

## What they're looking for

- Bachelor’s degree in Electrical Engineering or Computer Engineering, and 5+ years practical industry experience working with advanced process nodes (7nm or below)
- Deep hands-on proficiency with industry-standard physical design, timing, and sign-off tools (e.g., Innovus, Fusion Compiler, PrimeTime, RedHawk)
- Proven track record running logic synthesis, integrating compiled memory macros, and managing multi-voltage design techniques using power intent specifications (UPF/CPF)
- Exceptional debugging skills with a first-principles approach to navigating complex trade-offs between congestion, timing slack, and power density in highly utilized designs
- A highly collaborative mindset and a bias for action to push boundaries and co-design effectively with RTL and architecture teams
- An extensive track record of delivering high-performance SoCs, CPUs, GPUs, or AI accelerators through multiple successful production tape-outs

Tags: Personal AI Hardware