Associate Solutions Architect
DESCRIPTION
AI Field Lab at Amazon Web Services (AWS) welcomes fresh graduates applications in the area of active learning, meta-learning, continual learning, automatic machine learning and neural networks. We are a Beijng-Shanghai-Shenzhen based team of consisting applied scientists and developers working on Amazon Machine learning solutions. We enable a wide range of external and internal customers to save time and resources when training and deploying machine learning models by, for example, GenAI, Multi-modal, Open source models and business automation integration. We innovate in fundamental directions, while being guided by real customer pain points.
An Associate Solutions Architect with us will give you a unique experience of how current state-of-the-art research is invented and shaped to meet the needs of machine learning services offered in the cloud.
Your responsibilities
· Research and implement novel approaches, at the intersection between machine learning and optimization
· Understand our business problem and evaluate your solution accordingly
· Collaborate with developers and pave the way of bringing your solution into production systems
· Communicate and document your research according to the high standards, preferably with external scientific contributions
We focus on LLM, GenAI, Inference optimization, industry domain models and open models. Please include your academic transcript in your CV.
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills.
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BASIC QUALIFICATIONS
· Master or PhD student in computer science, machine learning, applied math, statistics, or related field (a candidate not in a PhD program could be considered if she/he has a strong publication record to demonstrate her/his research skills)
· 3+ years of hands-on experience in Python and/or C++ and/or Java
· Designing, running, and analyzing experiments
· Communication and presentation skills
· Problem solving ability
PREFERRED QUALIFICATIONS
· Publications in top conferences (NIPS, ICML, KDD, AISTATS or journals like JMLR)
· Contributions to open source projects
· Experience with LLM/VLM/NLP/Machine Learning
· Familiar with Cloud technologies, especially in GPU/Storage/Machine Learning tools
· Previous internships