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Sr. Machine Learning Engineer, AWS Applied AI Solution

Amazon

Sr. Machine Learning Engineer, AWS Applied AI Solution

Amazon

Seattle, WA, USA

·

On-site

·

Full-time

·

1mo ago

Compensation

$168,100 - $227,400

Benefits & Perks

Flexible PTO policy

Wellness benefits

Health, dental, and vision coverage

Annual team offsites

Required Skills

Python

Airflow

TensorFlow

As part of the AWS Applied AI Solutions organization, our vision is to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We accelerate our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. Our team combines Amazon's real-world experience with state-of-the art AI to create opinionated, turnkey solutions that are no-brainers to buy and easy to use.
We're building applied AI solutions that businesses love and trust. Our ambition is to become the partner companies rely on to run their business every day - putting AI to work to deliver better customer experiences, operational excellence, and faster innovation. We're a fast-moving, scrappy team building a new agentic product from the ground up. If bias for action is your favorite leadership principle, you'll fit right in.

The Role:

We're seeking a talented Senior Machine Learning Engineer with expertise in agentic system, production ML systems, and scalable deployment
architectures. You'll bridge the gap between state-of-art research and customer-facing products, contribute to our collaborative and innovative culture, and deliver production-ready ML solutions that raise the bar for the entire team.

  • What You'll Do
  • Work closely with Applied Scientists and cross-functional engineering teams to transform research code into robust, scalable production systems
  • Own end-to-end deployment at scale of Generative AI and ML methods, ensuring reliability and performance
  • Establish scalable, efficient, automated processes for large-scale data analysis, machine learning model development, model validation and serving
  • Research and implement innovative approaches for efficient model deployment, training, and optimization
  • Document processes and methods for both technical and non-technical audiences, ensuring knowledge transfer and best practices
  • Contribute to code reviews and maintain high engineering standards across the team
  • Mentor junior MLEs and actively participate in recruiting top talent to grow
    the team
  • Present outcomes and explain technical approaches to senior leadership, translating complex concepts into business impact

Basic Qualifications

  • 5+ years of non-internship professional software development experience
  • 5+ years of programming with at least one software programming language experience
  • 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • Experience as a mentor, tech lead or leading an engineering team
  • Knowledge of Python and/or C++ programming
  • Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques

Preferred Qualifications

  • 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • Bachelor's degree in computer science or equivalent
    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 - 168,100.00 - 227,400.00 USD annually

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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

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