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Applied Scientist, Prime Air Perception

Amazon

Applied Scientist, Prime Air Perception

Amazon

Graz, AUT

·

On-site

·

Full-time

·

1mo ago

Required skills

Python

Machine Learning

We’re working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve large-scale real world challenges, and provide visible benefit to end-users, this is your opportunity.

Come work on the Amazon Prime Air Team!

We're looking for an applied scientist who combines superb technical, research and analytical capabilities with a demonstrated ability to get the right things done quickly and effectively. We’re looking for someone who innovates and loves solving hard problems. You will work hard, have fun, and of course, make history!

The monthly gross salary according to the CBA is at least EUR 4.006. There is a willingness to make an overpayment, depending on qualification and professional experience.

Export Control License: This position may require a deemed export control license for compliance with applicable laws and regulations. Placement is contingent on Amazon’s ability to apply for and obtain an export control license on your behalf.

Key job responsibilities
Develop computer vision algorithms for autonomous drones.
Monitor systems in operation.
Manage ML ops pipelines.
Deep dive into data.
Build prototypes.
Port algorithms to real-time systems.

About the team
The Perception team of Prime Air develops Computer Vision algorithms that allow our drones to sense and avoid obstacles, allowing to fully autonomously deliver packages to customers in 30 minutes or less.

Basic Qualifications

  • Machine Learning
  • Computer Vision
  • Programming skills in C++, Python or similar

Preferred Qualifications

  • Publication track record in at least one major computer vision conference, such as CVPR, ECCV, ICCV or NeurIPs
  • Phd in ML, Computer Vision or related field
  • Experience in porting algorithms to computer and memory constraint embedded real-time systems

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 (https://www.amazon.jobs/en/privacy_page) 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 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.

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About Amazon

Amazon

Amazon

Public

Amazon.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

$1.5T

Valuation

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

4 data points

L2

L3

L4

L5

L6

L2 · Solution Architect L2

0 reports

$178,190

total per year

Base

$71,276

Stock

$89,095

Bonus

$17,819

$124,733

$231,647

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