As a Senior Applied Scientist, you'll innovate at the leading edge of on-device and privacy-first AI, bringing small and efficient language models to devices so that generative and conversational capabilities run locally without a round-trip to the cloud. You'll advance privacy-preserving machine learning, techniques like federated learning and on-device personalization that make models smarter without sensitive data ever leaving the device, and design the on-device inference and acceleration that lets these models run responsively across the specialized hardware inside everyday devices. And because not every problem is solved locally, you'll help shape the hybrid edge-cloud orchestration that intelligently decides what stays private on the device versus what escalates to the cloud, preserving both privacy and responsiveness. It's work that pushes the frontier of edge AI from research into products used by millions every day, where every model must be efficient, responsive, and trusted by design.
About the team
We're building up the Alexa Connections science team, so you'll join early and help shape its direction from the ground up. There's plenty of room to grow, deepening your expertise, mentoring others, or stepping into a tech lead role and setting the scientific vision for trusted, privacy-first communication. It's a rare chance to make a foundational impact on the technology, the team, and how millions of customers connect every day.
BASIC QUALIFICATIONS
- PhD, or Master's degree and 6+ years of building machine learning models for business application experience
- 4+ years of applied research experience
- Experience using managed ML/AI solutions
- Knowledge of programming languages such as C/C++, Python, Java or Perl
PREFERRED QUALIFICATIONS
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Background in multimodal communication technologies
- Familiarity with privacy-preserving ML (federated learning, differential privacy)
The base salary range for this position is listed below. As a total compensation company, Amazon's package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well-being. We thank all applicants for their interest, however only those interviewed will be advised as to hiring status.
CAN, ON, Toronto - 195,900.00 - 327,200.00 CAD annually
