This job is closed
We regret to inform you that the job you were interested in has been closed. Although this specific position is no longer available, we encourage you to continue exploring other opportunities on our job board.
Amazonposted 3 months ago
Full-time - Mid Level
Onsite - Leesburg, VA
Sporting Goods, Hobby, Musical Instrument, Book, and Miscellaneous Retailers
About the position
The Machine Learning Engineer position within the Data Platform Engineering Team at AWS focuses on enabling the US Intelligence Community to leverage their data through Machine Learning (ML) methods. The role involves architecting and implementing AWS cloud-native ML solutions, collaborating with data scientists and engineers, and optimizing data platforms for ML model training and inference. This customer-facing role requires a strong understanding of data architectures and the ability to lead projects to success.
Responsibilities
- Architect and implement innovative AWS cloud-native ML solutions that achieve customer business outcomes.
- Inspect, investigate, and understand customer data sources.
- Design and run experiments, and research new algorithms.
- Collaborate with data scientists and engineers to create data flows to and from models.
- Build data platforms that infuse ML into diverse missions.
- Develop and maintain key platforms needed for developing, evaluating, and deploying models for real-world applications.
- Investigate design approaches, prototype new technology, and evaluate technical feasibility.
Requirements
- Bachelor's degree in computer science or equivalent.
- 3+ years of non-internship professional software development experience.
- 2+ years of non-internship design or architecture experience of new and existing systems.
- Experience programming with at least one software programming language.
- Current, active US Government Security Clearance of TS/SCI with Polygraph.
Nice-to-haves
- Master's/PhD in Machine Learning and its practical applications.
- 3+ years of full software development life cycle experience.
- Experience with running A/B tests in production and knowledge of causal inference and other modern machine learning techniques.
- Experience in large scale AI and ML infrastructure and distributed training and inference for large language models.
Benefits
- Flexible work hours and arrangements
- Mentorship and career growth opportunities
- Inclusive team culture
- Work-life balance initiatives
