Staff Engineer: Multiphysics Packaging Co-Simulation

Absolics Inc; Covington (GA); On-site; USD 90,000 - 150,000; Full time

Benefits offered by this job

401K 6% matching

Healthcare 100% support

Life, STD, LTD 100% support

Cell phone allowance

PTO and self-development

Job summary

Absolics Inc. in Covington, GA seeks a Staff Engineer to drive advanced packaging mult physics modeling, covering mechanical and electrical co-simulation from die interconnects to buildup layers and system integration.

You will build predictive models, validate them against reliability data, and translate findings into actionable design rules for process and product teams. PhD or equivalent industry experience is preferred.

Qualifications

• Experience with FEA/thermo-mechanical and fracture analysis in multilayer stacks.
• Model interlayer stress, delamination, and cohesive-zone behavior.
• Characterize materials including dielectrics, metals, and substrates.
• Correlate simulations with SEM/CSAM and reliability data.
• Proficient in scripting with Python, MATLAB, or APDL.

Responsibilities

• Own simulation and interpretation of Multiphysics behavior across packaging architectures.
• Build and validate predictive models for mechanical and electrical performance.
• Translate simulation output into design rules for process and product teams.
• Perform FEA of interlayer stresses and reliability failure analysis.
• Correlate simulation results with physical failure analysis and reliability tests.

Skills

FEA modeling

Delamination modeling

Interface stresses

Material characterization

RDL knowledge

Failure mode analysis

Electro-thermal coupling

Packaging processes

Scripting (Python/MATLAB)

Education

PhD or equivalent industry experience

Tools

ANSYS

Abaqus

COMSOL

APDL

Job description

Absolics Inc. in Covington, GA seeks a Staff Engineer to drive advanced packaging mult physics modeling, covering mechanical and electrical co-simulation from die interconnects to buildup layers and system integration.

You will build predictive models, validate them against reliability data, and translate findings into actionable design rules for process and product teams. PhD or equivalent industry experience is preferred.