Principal AI Forward Deployment Engineer

Cadence; Oregon (WI); Hybrid; USD 130,000 - 160,000; Full time

Job summary

Cadence is seeking a Principal AI Forward Deployment Engineer in Oregon to lead the deployment of AI solutions in semiconductor design. In this role, you'll work closely with customers to operationalize AI tools across design workflows, turning theoretical applications into tangible productivity results.

The ideal candidate has over 10 years of semiconductor experience, exceptional communication skills, and an understanding of EDA processes. Relocation to Portland, OR may be necessary.

Qualifications

• 10+ years of hands-on semiconductor or chip design experience.
• Excellent communication skills with technical staff and leadership.
• Deep fluency with industry EDA flows and pain points.

Responsibilities

• Lead end-to-end deployment of Cadence AI solutions.
• Architect and tune workflows using LLMs.
• Be the trusted technical advisor to customers.

Skills

Hands-on semiconductor / chip design experience

Customer-facing communication

LLMs / AI tools application

Understanding of EDA flows

Education

BS/MS/PhD in EE, ECE, CS, or related discipline

Tools

Cadence AI tools

EDA tools

Job description

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.

Role Summary

Cadence is hiring a Principal AI Forward Deployment Engineer to embed with strategic semiconductor customers and operationalize Cadence's AI and agentic solutions across their end-to-end silicon design flows. This is a senior, customer-facing applied-AI role for an experienced chip-design practitioner who can translate front-end, physical-design, and methodology workflows into concrete, measurable productivity wins powered by flagship LLMs and Cadence AI tooling. You will be the trusted technical lead at the customer who turns 'AI for chip design' from a slideware promise into a deployed, adopted, and ROI-positive reality.

Why This Role Exists

• Customers are racing to apply LLMs and agentic AI to RTL, verification, implementation, and signoff – but they need a partner who deeply understands both their tape‑out flows and how to wield modern AI systems.
• This role owns the last mile: taking Cadence AI products (AgentStack, BU Super‑Agents, Cadence.AI / JEDAI platform, Cerebrus, Verisium, Allegro X AI, etc.) and making them work inside the customer's actual methodology, PDKs, compute fabric, and security boundaries.
• Success here directly accelerates customer tape‑outs, expands Cadence AI footprint, and feeds product roadmaps with real‑world signal.

What You Will Do

• Customer‑Embedded Deployment. Lead end-to-end deployment of Cadence AI / LLM‑powered solutions into the customer's silicon design flows – from RTL design and verification through synthesis, place‑and‑route, timing, power, signoff, and packaging.
• Applied AI for Chip Design. Architect and tune agentic workflows, prompt strategies, RAG pipelines, and tool‑calling patterns on top of flagship LLMs (Claude, GPT, Gemini, Llama, Qwen, etc.) to automate real EDA tasks – debug triage, constraint authoring, ECO loops, PD recipe tuning, methodology Q&A, log/report summarization, and design‑review copilots.
• Methodology & Flow Integration. Map customer methodology (FE design, DV, PD, CAD, signoff) onto Cadence AI building blocks; identify the highest‑ROI insertion points; build reference flows the customer's CAD/methodology team can own long‑term.
• Productivity & ROI Ownership. Define and report KPIs – cycle‑time reduction, engineer‑hours saved, iteration count, first‑pass success, license/compute efficiency. Own the story from pilot to production rollout across BUs and sites.
• Trusted Technical Advisor. Be the senior technical face to customer fellows, methodology leads, and design‑team principals. Run design reviews, technical readouts, and joint roadmaps. Translate customer pain into actionable feedback for Cadence R&D and AI platform teams.
• AI Systems Enablement (plus). Where needed, guide customer IT/InfoSec on secure LLM access patterns, on‑prem vs. gateway deployment, model routing, prompt/response logging, data‑egress controls, and GPU/inference capacity planning.
• Knowledge Multiplier. Capture playbooks, reference designs, and 'golden prompts' so every deployment compounds. Mentor field AEs, FAEs, and customer champions.

Must‑Have Qualifications

• BS/MS/PhD in EE, ECE, CS, or related discipline.
• 10+ years of hands‑on semiconductor / chip design experience in one or more of: front‑end design (RTL, micro‑architecture, DV), physical design / implementation (synthesis, P&R, timing, power, signoff), CAD / methodology / flow development, or design execution / project technical leadership on production tape‑outs.
• Deep, practitioner‑level fluency with industry EDA flows and pain points – you have personally shipped silicon and know where the cycle‑time and engineer‑time really go.
• Demonstrated ability to apply LLMs / AI tools to get real engineering work done (e.g., agent‑based debug, code/RTL generation assist, scripted automation, copilots, RAG over design docs/logs). Doesn't have to be your title – has to be your reflex.
• Excellent customer‑facing communication: equally credible with a staff RTL engineer, a PD methodology lead, and a CAD / design‑enablement leader.
• Bias to action, ownership, and shipping. Comfortable being the single throat‑to‑choke for a customer‑facing AI deployment.
• Based in or willing to relocate to Portland, OR; able to be on‑site at customer locations as the engagement requires.

Nice‑to‑Have / Bonus

• Prior experience as a Cadence / Synopsys / Siemens EDA customer power user, AE, or methodology architect.
• Hands‑on with at least one agentic framework (Claude Code, Cursor, LangGraph, AutoGen, custom MCP/tool‑calling stacks) applied to engineering workflows.
• Experience standing up secure enterprise LLM access (gateways, model routing, key management, audit logging) for engineering orgs.
• Familiarity with GPU/inference infrastructure, vLLM/TGI/Triton, fine‑tuning / LoRA / SLM workflows, and evaluation harnesses for code/EDA tasks.
• Background in low‑power, high‑performance, AI/ML, networking, automotive, or HPC silicon programs.
• Public artifacts – papers, talks, OSS, or internal champions – showing applied AI in EDA/silicon contexts.

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