Amazon Security Engineer II Application 

United States, Texas, Austin 

643440624

You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.In this role you will be capable of using GenAI and other techniques to design, evangelize, and implement and scale solutions for never-before-solved problems.Key job responsibilities
Collaborate with stakeholders to gather requirements and propose effective migration strategies.Providing technical guidance and troubleshooting support throughout project delivery.Sharing knowledge within the organization through mentoring, training, and creating reusable artifacts.About the team
Diverse Experiences
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.Why AWS
Work/Life BalanceMentorship and Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

PhD, or Master's degree and 5+ years of applied research experience
3+ years of building machine learning models for business application experience
Experience with programming in Java, Python or related language
Experience with neural deep learning methods and machine learning

Experience with LLM pre-training, fine-tuning, and evaluations
Familiarity with distributed training and acceleration method/implementation/library for LLMs or large-scale ML in general, e.g., DeepSpeed, Nemo-Megatron, PyTorch Lightning.
science backgrounds demonstrated by degrees in fields such as:
Mathematics
Statistics
Physics (various specializations)
Molecular Biology
Neuroscience
Experience manipulating large datasets using RDBMS or SQL-on-Hadoop technologies, and be familiar with machine learning or statistical libraries like SparkML, scikit-learn, caret, mlr, and MLlib