Applied Scientist, Alexa Edge AI

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Machine Learning Science

This role focuses on researching and developing next-generation machine learning models for computer vision, audio processing, and multimodal semantic understanding, with a strong emphasis on defining the science roadmap, delivering scalable solutions, and publishing research. The role involves technical leadership, end-to-end ownership of ML programs, and mentorship.

What you'd actually do

  1. 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.
  2. Own complex ML programs end-to-end — from identifying high-impact problems, designing data strategies and evaluation frameworks, through experimentation, optimization, and deployment at production scale.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • PhD, or Master's degree and 6+ years of applied research experience
  • 5+ years of building machine learning models for business application experience
  • Experience programming in Java, C++, Python or related language

Nice to have

  • 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

What the JD emphasized

  • publication record
  • top-tier venues
  • top-tier ML/CV/Audio conferences

Other signals

  • drive research and development of next-generation machine learning models
  • architect and drive development of advanced deep learning models
  • define the science roadmap
  • deliver solutions that operate at scale
  • deliver results that advance both the product and the broader field