Principal Modeling Engineer, Field Applications

Synopsys•Austin, TX

59d•$160,000 - $240,000•Onsite

About The Position

Synopsys is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services. We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow. You have spent years deep in semiconductor physics, building and validating models that have to predict real silicon behavior, not theoretical outcomes. The difference between a model that works in a lab and one that survives production at a foundry is something you have learned the hard way, and you know how to close that gap before a customer finds it. You are comfortable sitting with wafer data that does not make sense yet, pulling it apart until the story emerges, then translating that into a compact model someone can actually use in an OPC flow. Working across R&D, product teams, and customer sites does not intimidate you. You have been the person in the room who can explain why a calibration failed to an engineer in Taiwan at 6am, then turn around and brief a product manager on what needs to change in the next release. You do not wait for clean data or perfect specs. You ask the right questions, build what is needed with what you have, and move the work forward. At Synopsys, you will be the technical authority that field teams and customers rely on when modeling gets hard. The problems are real, the technology nodes are advanced, and what you build will directly affect whether a customer can tape out on time.

Requirements

M.S. or Ph.D. in Electrical Engineering, Physics, Semiconductor Engineering, or a related technical field
8+ years building, calibrating, or validating semiconductor device models in TCAD, SPICE, or lithography modeling contexts
Deep understanding of semiconductor device physics and how process technology choices affect model behavior and accuracy
Experience developing compact models and working through calibration methodologies that balance accuracy, runtime, and usability
Proven track record supporting customer-facing technical engagements where you were the expert in the room solving real deployment problems
Programming capability in Python, Tcl, Perl, or similar scripting languages is a strong plus, especially for automation and workflow development
Experience applying machine learning techniques to modeling or characterization work is a differentiator

Nice To Haves

You can walk into a customer meeting with incomplete wafer data and a calibration that failed overnight, and walk out with a plan that gets the engagement back on track
When a model does not match silicon, you do not guess. You methodically work through test structures, process assumptions, and measurement noise until you find the root cause
You know how to influence without authority. Product teams listen to you because your feedback is specific, grounded in field reality, and comes with a clear technical rationale
Mentoring happens naturally for you. When a field engineer is stuck on a modeling issue, you do not just fix it, you show them how to think through the next one
You can present a complex modeling tradeoff to a VP in two sentences without losing the nuance, and then turn around and debug a Tcl script with an engineer
Working across time zones and organizations does not slow you down. You are organized enough to manage multiple customer engagements, product discussions, and internal projects without dropping threads

Responsibilities

Calibrate and validate semiconductor models for advanced process nodes working directly with customer wafer data, test structures, and characterization results
Generate and maintain compact models used in customer OPC engagements, ensuring accuracy and performance across lithography workflows
Debug complex modeling issues at customer sites, working with Field Applications Engineers to resolve calibration failures, data quality problems, and deployment blockers
Build and maintain automated validation and regression environments that catch model quality issues before they reach customers
Collaborate with R&D and Product Engineering to translate field requirements into product enhancements, contributing directly to roadmap discussions and feature prioritization
Deliver technical training and enablement to customers, supporting evaluations, benchmarks, and proof-of-concept work that drives technology adoption
Develop best practices, methodology documentation, and workflow automation that improve modeling productivity across the field organization