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
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