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Thermal & Mechanical Engineer, Annapurna Labs - AI Silicon Packaging

OnsiteEngineering & technologyNot yet verified

United States, TX, Austin

Original posting date
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Nov 2, 2026

Saved job description

As a member of the Machine Learning Acceleration team you'll be responsible for the design and optimization of hardware in our data centers. You'll provide leadership in the application of new technologies to large scale server deployments in a continuous effort to deliver a world-class customer experience. This is a fast-paced, intellectually challenging position, and you'll work with thought leaders in multiple technology areas. You'll have high standards for yourself and everyone you work with, and you'll be constantly looking for ways to improve your products performance, quality and cost. We're changing an industry, and we want individuals who are ready for this challenge and want to reach beyond what is possible today.

Key job responsibilities

- Perform finite element simulations and analytical modeling to evaluate assembly, test, and reliability risks for advanced semiconductor packages

- Design and execute experiments on materials and packaging or system structures to validate performance and inform design decisions

- Develop risk evaluations for future Annapurna packages, providing data-driven recommendations that shape next-generation package designs

- Collaborate with Quality and Reliability, Package Design, Test, and System Design teams to optimize package architectures and resolve cross-functional technical challenges

- Communicate findings through clear documentation and design reviews, contributing to specifications and trade-off analyses that guide team decisions

A day in the life

Your primary focus is ensuring the thermal and mechanical integrity of Annapurna's semiconductor packages. On a given day you might run finite element simulations to stress-test a new package stackup, then head to the lab to collect experimental data on material behavior under thermal cycling. You'll review results with Package Design engineers, flag risks to the Quality and Reliability team, and refine your models based on real-world feedback — all while keeping an eye on how your work feeds into the next product generation.

About the team

Annapurna Labs, our organization within Amazon Web Services (AWS), designs custom silicon and software that accelerates cloud innovation. Within Annapurna, the Packaging team focuses on the thermal, mechanical, and materials engineering challenges that come with pushing semiconductor package performance forward. We work closely with Quality and Reliability, Package Design, Test, and System Design groups to deliver robust, manufacturable solutions at data-center scale.

Our team values collaboration and knowledge sharing. We are investing in advanced packaging technologies to support the growing demands of machine learning and compute workloads, and you will play a direct role in shaping those next-generation solutions.

Requirements: - Bachelor's degree in mechanical engineering or equivalent

- 5+ years industry experience in mechanical design of semiconductor-based systems – silicon, packaging, and system design

- 3+ years industry experience in advanced packaging of semiconductors

- Direct experience in designing and testing of test vehicles to assess future packaging architectures and evaluating properties of next-generation materials

- Expertise in optimizing in packaging architectures to meet quality, performance, and cost goals

- Industry experience of working with packaging component vendors and ODMs, and internal design teams on cross-boundary triaging, debugging, and resolving issues across organization

- Proficiency with simulation tools such as ANSYS, ABAQUS, COMSOL, FLOTHERM, ICEPACK or equivalent FEA/CFD software

Location & listing notes

Locations describe the saved posting and do not confirm residence eligibility. Confirm qualifications, travel, restrictions, and availability with the employer.

Source-listed locations: US, Austin, Texas.

Listed states: TX.

This saved listing has not passed full current-posting verification.

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