Alexa Connections is on a mission to become the world's most trusted communication agent — spanning calls, text, email, and the ever-expanding surfaces where people connect. We're building intelligence that keeps customers connected effortlessly while putting their trust and privacy first. As an Applied Scientist, you'll help build the smartest communications agent for people — an agent that understands intent, context, and relationships, and that acts on a customer's behalf to make every interaction feel effortless and genuinely helpful
Key job responsibilities You'll research, develop, and ship ML and AI solutions across the communication experience, reasoning over conversational context, intent and needs, and orchestrating large language models and agentic workflows that anticipate how, when, and with whom customers want to connect, and then get it done on their behalf. Whether it's surfacing the right message at the right moment, drafting a reply that sounds like the customer, remembering who matters most to them, or seamlessly carrying a conversation across calls, text, and email, you'll tackle ambiguous, open-ended problems at the intersection of applied science and real-world communication, turning advanced research into features that keep people effortlessly connected to the ones they care about.
About the team We're building up the Alexa Connections science team, so you'll join early and grow alongside it. You'll work closely with experienced scientists and engineers invested in your development, with plenty of mentorship, room to broaden your skills, and the chance to own meaningful problems as you grow. It's a supportive place to do impactful work early in your career, shaping how millions of customers connect every day.
Basic Qualifications
- 3+ years of building machine learning models or developing algorithms for business application experience
- PhD, or Master's degree and 2+ years of CS, CE, ML or related field experience
- Hands-on experience building agentic frameworks (e.g., tool-calling, function orchestration, memory, and multi-agent coordination) using modern LLMs
- Hands-on experience building RAG pipelines (embeddings, vector retrieval, context grounding) and personalization techniques for LLM-based systems
Preferred Qualifications
- PhD in computer science, machine learning, engineering, or related fields
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
- Knowledge of standard speech and machine learning techniques
- Knowledge of programming languages such as C/C++, Python, Java or Perl
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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.
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, BC, Vancouver - 149,300.00 - 249,300.00 CAD annually