Sr Software Dev. Engineer/MLE, Alexa AI

The Alexa AI team is looking for a passionate, talented, and inventive ML Engineer with a strong machine learning background, to build capabilities such as fine tuning, distillation, and LLM Inference.

As a ML engineer with the Alexa AI team, you will be responsible for machine learning platform focus on LLM training, production deployment, and optimizations to advance the state of LLMs. You will collaborate closely with Applied Scientists and other MLEs, leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate development of Generative Artificial Intelligence solutions.

Key job responsibilities
The ideal candidate is passionate about new opportunities and has a demonstrable track record of success in delivering new features and products. A commitment to team work, hustle, and strong communication skills (to both business and technical partners) are absolute requirements. Creating reliable, scalable, and high performance AI products requires exceptional technical expertise, a sound understanding of the fundamentals of Computer Science and Machine Learning. This person has thrived and succeeded in delivering high quality technology products/services in a hyper-growth environment.

Responsibilities-

- Will work with other team engineers to investigate design approaches, prototype new technology and evaluate technical feasibility.
- Work closely with Applied scientists to process data, scale machine learning models
- Will work in an Agile/Scrum environment to deliver high quality software.- 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- Experience developing large model hosting platforms, establishing frameworks, and scaling and optimizing inference system.
- Practical experienc using deep learning frameworks, such as pytorch and tensorflow in production systems.
- Experience developing and maintaining MLOps tool in large organizations.

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