採用
Required Skills
AWS
Amazon Leo is Amazon’s Low Earth Orbit satellite network. Our mission is to deliver fast, reliable internet connectivity to customers beyond the reach of existing networks. From individual households to schools, hospitals, businesses, and government agencies, Amazon Leo will serve people and organizations operating in locations without reliable connectivity.
The Leo Software Defined Networking (SDN) team designs, implements and operates the network virtualization stack and SDN control plane signaling. Our scope spans over beam planning, routing, and forwarding through our SDN Controller, Agents, and Applications that provides a high throughput telecom service comprised of Low Earth Orbit satellites, customer terminals, gateways, cloud services and terrestrial network infrastructure that connects into public and private networks.
We are looking for a talented Senior Applied Scientist to design and develop Network Observability solutions for an advanced global telecom service via both space and terrestrial networks. As a scientist on this team, you will collaborate with a mix of network engineers and software engineers to create novel mechanisms that increase our end-to-end observability tools and deliver high quality, secure and fault tolerant software used in Low Earth Orbit (LEO) satellites, ground gateways, and Consumer/Enterprise class customer terminals. You will define the long-term science roadmap for the team and its products. The candidate must have expertise with modern development practices and will have demonstrated the capability to deliver best-in-class software systems that solve some of today's hardest problems.
- Key job responsibilities
- Take responsibility for designing and delivering modern, flexible, scalable science solutions to complex challenges for operating and planning satellite constellations
- Work with peer teams and customers to design innovative science solutions to fulfill the business needs
- Write code for production cloud native software systems
- Utilize AWS and other Amazon technologies to deliver highly-available science solutions
- Help on-board and mentor new science team members
- Lead science roadmap definition efforts and decide what solutions to build
A day in the life
You will collaborate with various stakeholders to create the world’s most innovative products. You will understand operational challenges and existing blind-spots for network observability and be part of a team of scientists and engineers developing tools that fill these gaps. You will join our development and integration efforts and deliver high qualify software for production environments.
Basic Qualifications
- PhD, or Master's degree and 5+ years of CS, CE, ML or related field experience
- 5+ years of computer science fundamentals (object-oriented design, data structures, algorithm design, problem solving and complexity analysis) experience
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience in Java, C++, Python, or a related language
Preferred Qualifications
- Experience in written and verbal communication with the ability to present complex technical information in a clear and concise manner to executives and non-technical leaders
- Demonstrated experience with building LLM-powered agentic workflow, orchestration, and agent customization
- Demonstrated ability to identify and frame technical problems from broad product-level and business-level problem areas
- Track record of leading the design, implementation and delivery of scientifically complex solutions that span multiple teams
- Experience driving scientific agenda and technical strategy in a team, including building consensus on technical approaches and mentoring other scientists to improve their technical capabilities
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, Redmond - 167,100.00 - 226,100.00 USD annually
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About 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+
Employees
Seattle
Headquarters
Reviews
2.9
10 reviews
Work Life Balance
2.8
Compensation
3.7
Culture
2.5
Career
2.3
Management
2.1
35%
Recommend to a Friend
Pros
Good pay and compensation
Strong benefits package
Flexible scheduling options
Cons
Poor management and leadership
Limited growth and promotion opportunities
High stress and demanding work environment
Salary Ranges
2 data points
Junior/L3
L2
L3
L4
L5
L6
M3
M4
M5
M6
Mid/L4
Principal/L7
Senior/L5
Staff/L6
Director
Junior/L3 · Data Scientist L4
0 reports
$181,968
total / year
Base
-
Stock
-
Bonus
-
$154,672
$209,264
Interview Experience
10 interviews
Difficulty
3.7
/ 5
Duration
21-35 weeks
Offer Rate
20%
Experience
Positive 10%
Neutral 10%
Negative 80%
Interview Process
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Onsite/Virtual Loop
6
Team Matching
7
Offer
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Leadership Principles
Technical Knowledge
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