Amazon
Company
Applied Scientist II, Alexa AI
IN, KA, Bengaluru
Job Description
This job posting has expired and no longer accepting applications.
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI.
Key responsibilities include:
- Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI.
- Collaborate with cross-functional teams to architect and execute technically rigorous AI projects.
- Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines.
- Engage in effective technical communication (written & spoken) with coordination across teams.
- Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility.
- Publish research papers in internal and external venues of repute
- Support on-call activities for critical issues
Basic Qualifications:
- Master’s or PhD in computer science, statistics or a related field
- 2-7 years experience in deep learning, machine learning, and data science.
- Proficiency in coding and software development, with a strong focus on machine learning frameworks.
- Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems.
- Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.
- Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting.
- Papers published in AI/ML venues of repute
Preferred Qualifications:
- Track record of diving into data to discover hidden patterns and conducting error/deviation analysis
- Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
- The motivation to achieve results in a fast-paced environment.
- Exceptional level of organization and strong attention to detail
- Comfortable working in a fast paced, highly collaborative, dynamic work environment
Key responsibilities include:
- Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI.
- Collaborate with cross-functional teams to architect and execute technically rigorous AI projects.
- Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines.
- Engage in effective technical communication (written & spoken) with coordination across teams.
- Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility.
- Publish research papers in internal and external venues of repute
- Support on-call activities for critical issues
Basic Qualifications:
- Master’s or PhD in computer science, statistics or a related field
- 2-7 years experience in deep learning, machine learning, and data science.
- Proficiency in coding and software development, with a strong focus on machine learning frameworks.
- Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems.
- Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.
- Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting.
- Papers published in AI/ML venues of repute
Preferred Qualifications:
- Track record of diving into data to discover hidden patterns and conducting error/deviation analysis
- Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
- The motivation to achieve results in a fast-paced environment.
- Exceptional level of organization and strong attention to detail
- Comfortable working in a fast paced, highly collaborative, dynamic work environment
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