Senior Machine Learning Engineer, Neuro-Symbolic AI Labs
Amazon
Senior Machine Learning Engineer, Neuro-Symbolic AI Labs
Job Description
We are looking for Machine Learning Engineers (MLE) who aim to build super-intelligent AI systems that leverage proof assistants to guide learning and reasoning. Our neuro-symbolic AI technology is applied across a wide range of science and engineering domains within Amazon, and you will join the team at the forefront of this research. As a Senior MLE, you will play a pivotal role in shaping the definition, vision, and development of product features from beginning to end. You will: - Define and implement new neuro-symbolic applications that employ scalable and efficient approaches to solve complex problems. - Work in an agile, startup-like development environment, where you are always working on the most important stuff. - Deliver high-quality scientific artifacts. About the team We work closely with academia. Our team includes an Amazon Scholar in mathematics, and we maintain active research collaborations with faculty at leading CS departments (MIT, CMU).
Locations
- Boston, Massachusetts, United States
Salary
168,100 - 227,400 USD / yearly
Required Qualifications
- 5+ years of non-internship professional software development experience (experience, 5 years)
- 5+ years of programming with at least one software programming language experience (experience, 5 years)
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience (experience, 5 years)
- Experience as a mentor, tech lead or leading an engineering team (experience)
Preferred Qualifications
- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience (experience, 5 years)
- Bachelor's degree in computer science or equivalent (degree in computer science or equivalent)
Responsibilities
- Define and implement new neuro-symbolic applications that employ scalable and efficient approaches to solve complex problems.
- Work in an agile, startup-like development environment, where you are always working on the most important stuff.
- Deliver high-quality scientific artifacts.
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