Applied Scientist - Alexa Edge AI

We are looking for a Senior Applied Scientist to help establish and lead the technical direction of our newly formed team in Bangalore. In this role, you will drive the research and development of next-generation machine learning models spanning computer vision, audio processing, and multimodal semantic understanding. You will help define the science roadmap, tackle high-ambiguity problems across modalities, and deliver solutions that operate at scale. This is a rare opportunity to shape the technical vision, culture, and long-term research agenda of a greenfield site.
Responsibilities:
Model Development and Technical Leadership: Architect and drive development of advanced deep learning models for CV, audio understanding, and multimodal semantic fusion, setting the technical bar and defining best practices for the team.
End-to-End Ownership: Own complex ML programs end-to-end from identifying high-impact problems, designing data strategies and evaluation frameworks, through experimentation, optimisation, and deployment at production scale.
Research and Innovation: Define the science roadmap for your area; drive novel research directions in multimodal learning and deliver results that advance both the product and the broader field.
Publications and Thought Leadership: Maintain an active publication record at top-tier venues (e. g., CVPR, NeurIPS, ICASSP, ICCV, ACL) and represent the team externally in the research community.
Mentorship and Culture Building: Mentor scientists and engineers, raise the technical bar through hiring, and play a foundational role in establishing the Bangalore site's culture, processes, and scientific identity.
Requirements:
PhD, or a Master's degree and 6+ years of applied research experience.
5+ years of experience building machine learning models for business applications.
Experience programming in Java, C++, Python or a related language.
5+ years of hands-on experience building and deploying ML/DL models in computer vision, audio/speech processing, or multimodal learning.
Proven track record of leading technical initiatives from conception to production impact.
Deep expertise in modern neural architectures (transformers, diffusion models, foundation models).
Experience defining science roadmaps and influencing cross-functional priorities.
Familiarity with distributed training at scale, model compression, and low-latency inference.
strong publication record at top-tier ML/CV/Audio conferences.
Track record of mentoring junior scientists and building high-performing research teams.

Experience
10-14 yrs