STAFF R&D/PRODUCT DVL ENGINEER

Job Overview

The Staff Engineer – 3D Tolerance Analysis & Dimensional Management is a technical leader within the Mechanical Engineering Center of Excellence (COE), supporting electrical interconnect products for data center and AI infrastructure applications. 

This role develops and deploys predictive 3D variation-analysis capabilities, serves as the subject matter expert in GD&T, statistical tolerance methods, and dimensional management, and helps product teams identify and mitigate dimensional risks early in development. 

Job Requirements

Analysis & Problem Solving 

Lead complex 3D tolerance stack-up and variation analyses for precision interconnect products and assemblies. 

Resolve dimensional issues involving mating alignment, contact engagement, positional accuracy, interference, clearance, gaps, datum structures, and assembly sequence. 

Apply worst-case, RSS, statistical, Monte Carlo, and sensitivity methods to identify dominant risk contributors and improve design robustness. 

Support high-complexity design challenges where conventional 1D or manual stack-ups are insufficient. 

Methods, Tools & Capability Development 

Develop scalable 3D dimensional-analysis methods, workflows, templates, libraries, and automation. 

Evaluate and benchmark tools such as 3DCS, CETOL 6σ, or equivalent technologies for accuracy, usability, CAD integration, and scalability. 

Translate functional and manufacturing inputs into predictive variation models, incorporating process capability, assembly variation, and measurement data. 

Verify models using prototype and production data and integrate with FEA, contact mechanics, optimization, or statistical methods where valuable. 

Identify capability gaps and recommend tool deployment, complementary methods, automation, and future investments. 

GD&T, Standards & Dimensional Management 

Serve as the COE expert in GD&T, ASME Y14.5, datum strategy, tolerance schemes, and dimensional management. 

Connect functional requirements to dimensional requirements, 3D variation analysis, GD&T/PMI, manufacturing, and measurement. 

Balance product performance, manufacturing capability, quality, and cost when defining tolerance strategies. 

Create standards, quality criteria, templates, reference problems, and guidance for CTF/CTQ characteristics and method selection. 

Technical Consulting & Leadership 

Partner with product, manufacturing, tooling, quality, metrology, reliability, and simulation teams from concept through qualification and root-cause investigation. 

Use process capability and CMM, optical metrology, CT, or other measurement data to correlate predicted and actual variation. 

Communicate findings as clear, actionable design recommendations. 

Mentor engineers, lead workshops and design reviews, and build a dimensional-engineering community of practice.

Mandatory Skills:

Required 

Bachelor’s or master’s degree in mechanical engineering, Manufacturing Engineering, Mechatronics, or a related discipline. 

Typically, 8+ years of relevant engineering experience. 

Expertise in 3D tolerance analysis, GD&T/ASME Y14.5, datum structures, assembly variation, and dimensional measurement. 

Proficiency with 3DCS, CETOL 6σ, or equivalent tools and with worst-case, RSS, statistical, Monte Carlo, sensitivity, and process-capability methods. 

Demonstrated success developing engineering methods, workflows, standards, or technical capabilities and leading complex cross-functional problem solving. 

Preferred 

Experience with precision electromechanical products such as electrical connectors, high-speed interconnects, cable assemblies, sockets, or electronics packaging. 

Experience in data center, AI infrastructure, networking, server, storage, or high-performance-computing applications. 

Experience benchmarking simulation tools; integrating tolerance analysis with FEA, contact mechanics, or optimization; using dimensional metrology; or developing engineering automation. 

Role Focus 

This is a COE capability-development and technical-expert role, focused on advancing methods and tools, solving high-complexity dimensional problems, establishing best practices, and enabling product teams to apply predictive dimensional management earlier in product development.