Packaging Research and Development Engineer
$133,800–$188,890 year
On-sitePhoenix, Arizona, United States
Job Summary
Define equipment configurations, process specifications, and integrated process flows for new substrate packaging technologies. Conduct comprehensive Design of Experiments to characterize process windows and interactions between equipment, materials, and processes. Drive continuous improvements in process capability, quality, yield, automation, and productivity. Lead cross-functional teams and collaborate with suppliers to evaluate and optimize new technologies. Apply advanced analytical techniques to evaluate organic and inorganic materials and leverage statistical tools to derive insights from complex datasets. Requires a PhD in a relevant engineering or science field with 1+ years of experience. Full-time on-site role in Chandler, Arizona, with an annual salary range of $133,800.00 to $188,890.00 USD.
Required Qualifications
- PhD degree in Mechanical Engineering, Chemical Engineering, Materials Science, Physics, Chemistry, or a closely related field
- 1+ years of relevant experience in a science or engineering field (obtained through academic research, internships, or professional experience)
- Is open to occasional travel (less than 5%) to engage with suppliers and partners
Desired Qualifications
- 1+ years of experience in: Engineering troubleshooting and analytical problem-solving on integrated technology challenges, working within broad, cross-functional engineering teams
- Owning and operating process tools, with knowledge of Statistical Process Control (SPC), Process Change Systems (PCS), Request for Change (RFC) processes, equipment troubleshooting, statistical design of experiments, and process development
- Variety of organic and inorganic materials and processes, including characterization techniques such as DSC, TGA, D/TMA, Titration, ICP-MS, FTIR, XPS, TOF-SIMS, EDX, and SEM
- Statistical data analysis tools including JMP, JSL, and Python; familiarity with relational database structures and machine learning concepts
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