Applied Scientist, Alexa Edge AI

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

Applied Scientist role focused on designing, developing, and deploying multimodal ML models (CV, audio, speech) for edge and cloud deployment. The role involves full ML lifecycle ownership, research, and contributing to publications. It's a founding member position in a new Bangalore site, requiring collaboration with engineers and scientists to productionize algorithms.

What you'd actually do

  1. Design and build deep learning models for computer vision, audio understanding, and multimodal semantic fusion — including architectures that enable joint reasoning across visual, auditory, and textual modalities.
  2. Own the full ML lifecycle — from problem formulation, data strategy, and annotation design through experimentation, evaluation frameworks, model optimization, and deployment at scale.
  3. Stay at the frontier of CV, audio ML, and multimodal learning; identify and apply SOTA techniques and contribute to the scientific community through papers at top-tier venues (CVPR, NeurIPS, ICASSP, ICCV, ACL).
  4. As a founding member of the Bangalore site, help hire, onboard, and establish the technical practices that define the team's culture.

Skills

Required

  • PhD, or Master's degree and 3+ years of CS, CE, ML or related field experience
  • 1+ years of building models for business application experience
  • Experience programming in Java, C++, Python or related language
  • Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms

Nice to have

  • Knowledge of standard speech and machine learning techniques
  • PhD or work experience in a relevant field (CV, Audio, multimodal language models)
  • Experience with distributed training, model compression, and inference optimization (e.g. pruning, quantization, distillation, etc.)
  • Publications at top-tier ML/CV/Audio conferences
  • Demonstrated ability to work in ambiguous, fast-paced environments and define technical roadmaps independently

What the JD emphasized

  • publications at top-tier venues
  • publications at top-tier ML/CV/Audio conferences

Other signals

  • design, develop, and deploy state-of-the-art machine learning models
  • work at the intersection of multiple modalities
  • founding member of a brand-new site