At a Glance
- Tasks: Develop cutting-edge machine learning solutions for Amazon's transportation systems.
- Company: Join Amazon, a leader in e-commerce and logistics, focused on customer-centric innovation.
- Benefits: Enjoy a diverse workplace, opportunities for growth, and the chance to impact sustainability.
- Why this job: Be part of a team that shapes the future of delivery while tackling real-world challenges.
- Qualifications: PhD or Master's in CS, CE, ML; experience in model building and programming required.
- Other info: Amazon values diversity and offers accommodations for applicants with disabilities.
The predicted salary is between 43200 - 72000 £ per year.
Applied Scientist, ATS Machine Learning & Engineering
Are you interested in building state-of-the-art machine learning systems for the most complex, and fastest growing, transportation network in the world? If so, Amazon has the most exciting, and never-before-seen, challenges at this scale (including those in sustainability, e.g. how to reach net zero carbon by 2040).
Amazon’s transportation systems get millions of packages to customers worldwide faster and cheaper while providing world class customer experience – from online checkout, to shipment planning, fulfillment, and delivery. Our software systems include services that use tens of thousands of signals every second to make business decisions impacting billions of dollars a year, that integrate with a network of small and large carriers worldwide, that manage business rules for millions of unique products, and that improve experience of over hundreds of millions of online shoppers.
As part of this team you will focus on the development and research of machine learning solutions and algorithms for core planning systems, as well as for other applications within Amazon Transportation Services, and impact the future of the Amazon delivery network. Current research and areas of work within our team include machine learning forecasting, planning systems and robust decision making on large networks, as well as uncertainty quantification, generative models on graphs and ml explainability, among others.
We are looking for an Applied Scientist with a strong academic background in the areas of machine learning, time series forecasting, and/or optimization.
At Amazon, we strive to continue being the most customer-centric company on earth. To stay there and continue improving, we need exceptionally talented, bright, and driven people. If you’d like to help us build the place to find and buy anything online, and deliver in the most efficient and greenest way possible, this is your chance to make history.
About the team
The EU ATS Science and Technology (SnT) team owns scalable algorithms, models and systems that improve customer experience in middle-mile. We work backwards from Amazon’s customers aiming to make transportation faster, cheaper, safer, more reliable and ecologically sustainable.
BASIC QUALIFICATIONS
- PhD, or a Master’s degree and experience in CS, CE, ML or related field
- Experience in building models for business application
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
PREFERRED QUALIFICATIONS
- Experience using Unix/Linux
- Experience in professional software development
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice ( to know more about how we collect, use and transfer the personal data of our candidates.
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 for more information.
Posted: December 19, 2024 (Updated 8 days ago)
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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Applied Scientist, ATS Machine Learning & Engineering employer: Amazon
Contact Detail:
Amazon Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied Scientist, ATS Machine Learning & Engineering
✨Tip Number 1
Familiarize yourself with the latest advancements in machine learning, especially in areas like time series forecasting and optimization. This knowledge will not only help you understand the challenges Amazon faces but also allow you to discuss relevant solutions during your interview.
✨Tip Number 2
Engage with the machine learning community by attending conferences or webinars. Networking with professionals in the field can provide insights into current trends and may even lead to valuable connections that could benefit your application.
✨Tip Number 3
Showcase any experience you have with programming languages mentioned in the job description, such as Java, C++, or Python. Be prepared to discuss specific projects where you applied these skills, as practical examples can make a strong impression.
✨Tip Number 4
Research Amazon's commitment to sustainability and how it relates to their transportation services. Being able to articulate how your work can contribute to their goal of reaching net zero carbon by 2040 will demonstrate your alignment with their mission.
We think you need these skills to ace Applied Scientist, ATS Machine Learning & Engineering
Some tips for your application 🫡
Highlight Relevant Experience: Make sure to emphasize your academic background and any practical experience in machine learning, time series forecasting, and optimization. Use specific examples from your past work or research that demonstrate your expertise in these areas.
Showcase Your Technical Skills: Clearly list your programming skills, especially in languages like Java, C++, and Python. Mention any experience you have with algorithms, data structures, and high-performance computing, as these are crucial for the role.
Include Publications and Patents: If you have any publications in top-tier peer-reviewed conferences or journals, or patents related to your work, be sure to include them in your application. This will help establish your credibility and expertise in the field.
Tailor Your Application: Customize your CV and cover letter to reflect the specific requirements and responsibilities mentioned in the job description. Show how your skills and experiences align with Amazon's goals, particularly in improving customer experience and sustainability.
How to prepare for a job interview at Amazon
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning, time series forecasting, and optimization. Highlight specific projects where you've built models for business applications, and be ready to explain the algorithms and techniques you used.
✨Demonstrate Problem-Solving Abilities
Expect to face technical challenges during the interview. Practice solving problems related to algorithms, data structures, and numerical optimization. Think aloud as you work through these problems to showcase your thought process.
✨Discuss Your Research and Publications
If you have patents or publications, make sure to bring them up. Discuss the impact of your research and how it relates to the role. This will demonstrate your expertise and commitment to advancing the field of machine learning.
✨Emphasize Your Passion for Customer-Centric Solutions
Amazon values a customer-centric approach. Be ready to discuss how your work in machine learning can improve customer experiences in transportation. Share examples of how you've prioritized user needs in your previous projects.