Thinking Machines LabRemote · San Francisco$350k-$475k4h ago
AmazonPosted 4mo ago
Applied Scientist, Alexa Edge AI at Amazon scores 99 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.
AI in this role
Key job responsibilities
Model Development: 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.
End-to-End Ownership: Own the full ML lifecycle — from problem formulation, data strategy, and annotation design through experimentation, evaluation frameworks, model optimization, and deployment at scale.
Research & Innovation: Stay at the frontier of CV, audio ML, and multimodal learning; identify and apply cutting-edge techniques and contribute to the scientific community through papers at top-tier venues (CVPR, NeurIPS, ICASSP, ICCV, ACL).
Mentorship & Culture Building: As a founding member of the Bangalore site, help hire, onboard, and establish the technical practices that define the team's culture.
A day in the life
An Applied Scientist with the Alexa Edge AI team will support science solution design, run experiments, research new algorithms, and find new ways of optimizing the customer experience; while setting examples for the team on good science practice and standards. Besides theoretical analysis and innovation, an Applied Scientist will also work closely with talented engineers and scientists to put algorithms and models into production.
About the team
The Alexa Edge AI team has a mission to deliver best in class, resource efficient multimodal AI models in support of various perception (vision, audio and speech) and semantic understanding based applications for devices like Echo Show series within Amazon.
Basic qualifications
- 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
Preferred qualifications
- Knowledge of standard speech and machine learning techniques
- Experience with video and image processing and compression algorithms and standards, computer vision and/or machine learning
- Experience creating and delivering written and oral communications for technical and non-technical audiences
- Experience with distributed training, model compression, and inference optimization (e.g. pruning, quantization, distillation, etc.)
- Demonstrated ability to work in ambiguous, fast-paced environments and define technical roadmaps independently
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- Walk me through a computer vision problem you solved, from raw data to a deployed model.
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: Computer Vision. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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