Amazon
Sr. Package Engineer, Annapurna Labs - AI Silicon Packaging
$185K–$250K
Tempe, AZ +1 · Senior · Full-time · Posted Oct 20, 2026
- Location
- Tempe, AZ
Austin, TX - Experience
- 10+ years of related industry experience is an alternative to a bachelor's degree. Preferred alternatives: 8+ years with a master's or 6+ years with a PhD.
- Education
- Bachelor's in Materials Science, Mechanical Engineering, Electrical Engineering, or Chemical Engineering, or 10+ years of industry experience in a related field.
About the job
Develops and qualifies advanced packaging technologies for next-generation Trainium AI systems, from concept through volume production.
What you'll do
- Drive package technology development across design, materials, processes, yield, reliability, and system integration.
- Coordinate with suppliers and OSATs on validation, testing, qualification, risk reduction, and root-cause resolution.
- Evaluate technology tradeoffs and represent packaging in cross-functional design reviews and program checkpoints.
What they ask for
- Experience coordinating technical teams, peers, and business stakeholders.
- Bachelor's in Materials Science, Mechanical Engineering, Electrical Engineering, or Chemical Engineering, or 10+ years of industry experience in a related field.
- Expertise in substrate/PCB technology, interposer technology, or package and board assembly technology.
- Track record of driving technology development from concept through qualification.
Nice to have
- Master's in a listed engineering field or 8+ years of related industry experience; or PhD in a listed field or 6+ years of related industry experience.
- Familiarity or experience with package-level SI/PI, advanced substrate/PCB or interposer technologies, assembly processes, or co-packaged optics.
Pay
USD 185,200.00 - 250,000.00 annually base salary (USA, AZ, Tempe); USD 185,200.00 - 250,000.00 annually base salary (USA, TX, Austin)
Tools
Trainium, OSATs, HDI, CoWoS-R/L, RDL, TSV, flip-chip, hybrid bonding
AI focus
Develops packaging technologies for next-generation Trainium AI accelerators and systems.
Model-written summary · the original posting is authoritative