AmazonPosted 4mo ago

Applied Science Manager, Alexa Edge AI

Applied Science Manager, Alexa Edge AI at Amazon scores 94 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.

IN, KA, Bengaluruseniorfull-time

AI in this role

computer-vision

Are you ready to build something extraordinary from the ground up? We're looking for a seasoned Applied Science Manager to establish and lead a brand-new team in Bangalore, India, within Alexa Edge AI. This is a rare greenfield opportunity to shape the future of ambient intelligence by pioneering breakthroughs in computer vision, acoustic modeling, and multimodal semantic understanding that will power hundreds of millions of Alexa-enabled devices worldwide.

As an Applied Science Manager, you will architect and scale a world-class applied science team that pushes the boundaries of what's possible at the intersection of edge and cloud AI. From enabling seamless Visual ID that recognizes who's in the room, to crafting ultra-low-latency wake word detection that works flawlessly in noisy environments, to building multimodal models that build deep semantic understanding — your work will directly define how Alexa perceives, understands, and interacts with the physical world.

You'll operate at the frontier of on-device ML, tackling hard constraints in compute, memory, and power while delivering experiences that feel magical to customers. If you thrive on ambiguity, love building high-performing teams from scratch, and want to ship science that touches millions of lives daily — this is your moment.

Key job responsibilities
Establish and grow a high-caliber applied science team from the ground up at our new Bangalore site, defining the team's charter, culture, hiring bar, and technical roadmap

Recruit, mentor, and develop top-tier scientists and engineers across computer vision, speech/acoustics, and multimodal ML disciplines
Foster a culture of scientific rigor, rapid experimentation, customer obsession, and operational excellence

Drive R&D of privacy preserving edge solutions like Visual recognition and Acoustic Modeling (Wake Word & Audio Intelligence) optimized for edge deployment on resource-constrained hardware (custom silicon, DSPs, NPUs).

Define and execute strategies for optimizing latency, privacy, accuracy, and cost while
collaborating with hardware and silicon teams to co-design next-generation AI accelerators and model architectures

Own the end-to-end lifecycle from research ideation through experimentation, prototyping, and production deployment at scale
Establish robust benchmarking, A/B testing, and metrics frameworks to measure real-world impact
Partner closely with engineering, product, and UX teams to translate scientific breakthroughs into delightful customer experiences

Shape the long-term science and technology roadmap for Alexa's perceptual AI capabilities
Represent the team in org-wide science reviews, patent filings, and publications at top-tier venues (NeurIPS, ICML, CVPR, ICASSP, etc.)

Build strong cross-site collaboration with teams in Sunnyvale, Boston and other global locations

A day in the life
As an Applied Science Manager in Alexa Edge AI, you'll split your time between deep technical engagement and people leadership — reviewing experiment results, debating model architectures with your scientists, guiding on trade-offs, and connecting with cross-site partners to align on roadmap priorities and influence org-wide direction. Initially, a significant portion of your energy goes toward building the team itself: interviewing exceptional candidates, calibrating the hiring bar, coaching scientists on career growth, and shaping the culture of a brand-new site. You stay hands-on with the research landscape, refine your science roadmap, and ensure your team has clear priorities — all while context-switching fluidly between being a technical thought leader, a strategic voice in leadership forums, and a mentor to your growing team.

No two days are the same — but every day, you're building a team, pushing science forward, and shipping intelligence to the edge.

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) based applications for Echo Family of Devices within Amazon.

Basic qualifications

- PhD, or Master's degree and 8+ years of applied research experience
- 3+ years of building machine learning models for business application experience
- Experience managing and deploying ML products
- Deep expertise in at least one of: computer vision, acoustic/speech modeling, or multimodal learning

Preferred qualifications

- Experience in building and developing a high performance team
- Experience with multimodal LLM for visual or speech understanding
- Experience with on-device/edge ML deployment and optimization
- Publication track record at top-tier ML/CV/Speech conferences

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.

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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

Computer Vision

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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