招聘

Systems Modeling Mechanical Engineer, Fleet Remediation Engineering
Seattle, WA, USA
·
On-site
·
Full-time
·
5d ago
AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help.
You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion.
As a Systems Modeling Mechanical Engineer, you will bridge the gap between physical infrastructure and virtual simulation. You will be responsible for creating, maintaining, and validating high-fidelity models of our data center cooling systems, using these models to optimize performance, predict failures, and validate innovative product designs (such as advanced liquid cooling or high-density air-handling units) before deployment.
If you are passionate about innovative projects at hyper-scale and meet these qualifications, this role is for you!
- Key job responsibilities
- Build and maintain high-fidelity system models of data center mechanical systems — including cooling infrastructure (CRAC/CRAH, DAHU, chillers, liquid cooling) and airflow environments — continuously calibrated against physical test data and field measurements.
- Develop and execute CFD simulations to predict thermal performance, pressure drop, airflow distribution, and failure modes across a range of operating conditions.
- Automate simulation workflows and "what-if" scenario testing using Python, MATLAB, or equivalent scripting tools to accelerate design iteration.
- Lead pre-deployment validation of new and FOAK mechanical products — including rear-door heat exchangers, cold plates, DLC systems, high-density air handling units, and advanced rack architectures.
- Design and execute Hardware-in-the-Loop (HIL) lab testing that replicates realistic datacenter loading scenarios, validating system behavior under conditions that mirror Day 1 field deployment.
- Define and own Design Verification Plans (DVPs) and test procedures for prototype hardware, covering thermal, pressure, flow, and transient response testing.
- Perform Root Cause Analysis (RCA) on design failures by reproducing failure modes in simulation or the physical lab, producing data-backed validation reports that support deployment sign-off decisions.
- Partner with cross-functional teams to validate integrated system performance and build simulation-based business cases for new technology adoption.
- Develop institutional validation frameworks — repeatable test methodologies, simulation standards, and acceptance criteria — that scale across datacenter programs.
- Ensure deployed systems are validated against realistic failure scenarios, load transients, and edge-case conditions to support Day 1 operational readiness.
About the team
Why AWS
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Diverse Experiences
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
Inclusive Team Culture:
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and Amaze Con conferences, inspire us to never stop embracing our uniqueness.
Mentorship and Career Growth:
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Basic Qualifications
- Knowledge of mechanical or electrical systems design
- Education: Master’s in mechanical engineering, Thermal Engineering, or a related field.
- Experience: 6+ years of experience in data center design, construction, or operations, with a strong focus on thermal modeling.
- Modeling Skills: Deep proficiency in CFD software (e.g., Ansys Fluent, Cadence, or similar).
- Scripting: Proficiency in Python, MATLAB, or C++ for data analysis and automation.
- Validation: Hands-on experience with mechanical test fixtures, sensors, and data acquisition systems (DAQ).
- Excellent communication and report-writing skills.
Preferred Qualifications
- Experience in designing/testing, high-density, or liquid cooling systems.
- Knowledge of BIM/Revit for facility layout.
- Familiarity with ASHRAE guidelines for data center thermal management.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 116,800.00 - 160,000.00 USD annually
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关于Amazon

Amazon
PublicAmazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.
10,001+
员工数
Seattle
总部位置
$1.5T
企业估值
评价
3.4
10条评价
工作生活平衡
2.3
薪酬
4.2
企业文化
3.1
职业发展
3.8
管理层
2.7
65%
推荐给朋友
优点
Great benefits and competitive compensation
Learning opportunities and career advancement
Good teamwork and colleagues
缺点
High pressure and long hours
Poor work-life balance
Toxic work culture and high turnover
薪资范围
4个数据点
L2
L3
L4
L5
L6
L2 · Data Analyst L2
0份报告
$108,330
年薪总额
基本工资
$43,332
股票
$54,165
奖金
$10,833
$75,831
$140,829
面试经验
6次面试
难度
4.0
/ 5
时长
21-35周
体验
正面 0%
中性 17%
负面 83%
面试流程
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Technical Interview
6
Onsite/Virtual Interviews
常见问题
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
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