Hiring for fresher entry level Machine Learning Associate at Amazon

Position:

Amazon is a global leader in technology and innovation, revolutionizing the way people live and work. Our mission is to be Earth's most customer-centric company, where customers can find and discover anything they might want to buy online. As a part of our continued commitment to pushing the boundaries of technology, we are heavily investing in Artificial Intelligence (AI), particularly in the area of Generative AI. We believe AI has the power to address some of the world's most complex and pressing challenges, making it the most transformative technology of our time. Our focus is on the responsible development and deployment of large language models (LLMs) across various domains, enhancing human-technology interaction to create a future where AI plays a pivotal role in improving everyday life. With a strong emphasis on innovation, integrity, and excellence, Amazon continues to lead the charge in developing AI technologies that are both practical and ethical.

As an ML Data Associate-II at Amazon, you will play a crucial role in the foundational processes of AI development. Your primary responsibility will be to handle a diverse range of data types, including text, audio, speech, images, and videos. You will be instrumental in delivering high-quality labeled data, which is essential for training and improving AI models. Your role requires a combination of analytical thinking, attention to detail, and adaptability, as you will work with various data workflows and transition seamlessly between different labeling tasks. You will be expected to maintain strict confidentiality, uphold high standards of data quality, and contribute to continuous improvement efforts by identifying and addressing process challenges. Your work will directly impact the development of AI technologies, making this a highly impactful and rewarding position. You will collaborate closely with Machine Learning (ML) Science and Modelling teams, providing them with the critical data needed to enhance AI capabilities in speech, audio, visual recognition, and language understanding.

Educational Background:
- Bachelor's degree in any field of study.
Experience:
- 0-2 years of work experience, with a demonstrated ability to execute tasks effectively.
Research Skills:
- Ability to effectively research, comprehend, and evaluate diverse subject matter across various domains.
- Proven capacity to leverage open-source resources effectively for comprehensive research purposes.
Adaptability:
- Ability to adapt well to fast-paced environments with changing circumstances, direction, and strategy.
Ownership and Drive:
- A strong sense of ownership and drive, with a willingness to accept the challenge of daily deadlines.
Preferred Qualifications:
- Bachelor’s or Master’s degree preferred.
- Prior exposure to US culture and experience in the Machine Learning domain will be an added advantage.
- Willingness to multi-task and handle various responsibilities simultaneously.
- Excellent computer skills with a typing speed of over 50 words per minute.
- Language proficiency in verbal, writing, reading, and comprehension at a minimum B2 level in the CEFR framework.

Confidentiality:
- Strict adherence to confidentiality guidelines is mandatory in this role.
Data Handling:
- The role involves working with a wide range of data types, including text, audio, speech, images, and videos.
- You will be responsible for delivering high-quality labeled data using in-house tools, software, and guidelines.
Analytical and Critical Thinking:
- Demonstrate strong analytical and critical thinking capabilities while evaluating various forms of data and making informed decisions.
Decision-Making:
- Exercise logical decision-making skills, especially when dealing with ambiguous information.
Agility and Attention to Detail:
- Be agile in transitioning between different labeling workflows while maintaining a high level of attention to detail.
Expertise in Data Execution:
- Demonstrate expertise in handling the end-to-end data execution process related to labeling tasks, with minimal dependence on work instructions.
Quality Improvement:
- Contribute to root cause analysis, identify error patterns, and propose solutions to improve the quality of labeling tasks.
Process Challenges:
- Highlight process challenges in Standard Operating Procedures (SOPs) and tools, suggesting changes as needed.
Judgment and Ownership:
- Exhibit high judgment in balancing process adherence and taking ownership to analyze and provide resolutions.