AmazonPosted 4w ago

Sr. Applied Scientist, Alexa Smart Home

Sr. Applied Scientist, Alexa Smart Home 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.

CA, BC, Vancouverseniorfull-timeCAD 196k-CAD 327k

AI in this role

tensorflowscikit-learn

Alexa Smart Home Science builds the intelligence that lets customers control and automate their homes naturally, spanning voice understanding, proactive automation, habit and preference learning, and the LLM/agentic systems that power the next generation of ambient home experiences. We are looking for a Senior Applied Scientist to own the scientific direction of a major workstream, turning ambiguous, open-ended problems into production systems that measurably improve the customer experience across millions of homes.
You will define the research agenda for your area, partner deeply with engineering to bring models into production, and raise the scientific bar
across the broader team. You will work at the intersection of large language models, agentic orchestration, personalization, and real-world heterogeneity across homes, devices, and occupants. This is a hands-on role: you will frame the problems, build the models, guide the system design, and mentor other scientists, while influencing product and technical roadmaps beyond your immediate team.

Key job responsibilities
- Independently identify, frame, and solve ill-defined research problems tied to broad Smart Home problem areas, delivering with limited guidance.
- Define system-level technical requirements and partner with engineering teams to adapt scientific techniques to meet production constraints (latency, cost, reliability), making appropriate tradeoffs.
- Own the science strategy and roadmap for a workstream; anticipate future business and technical requirements.
- Design and run rigorous experimentation and evaluation to validate approaches and quantify customer/business impact.
- Build consensus on scientific approach and best practices across multiple teams; influence product features and technical direction beyond your own team.
- Actively mentor and develop other scientists, and raise the bar in hiring.
- Contribute to the internal and external scientific community through publications, patents, and dissemination of scientific artifacts.

A day in the life
No two days look alike, but the throughline is owning a scientific problem from question to production impact. You might dig into evaluation results to find where the experience falls short, frame a hypothesis, and prototype with LLMs or agentic techniques. You will pair with engineering to turn a promising model into a production-ready design, weighing latency, cost, and reliability tradeoffs. Expect alignment with product managers on customer problems, working sessions with fellow scientists, and time mentoring teammates. Your customers are Alexa smart home users across millions of households; your stakeholders span product, engineering, and partner science teams.

About the team
We are the science team behind Alexa's smart home experience. We are applied scientists and science engineers making the home genuinely intelligent: anticipatory, personalized, and effortless. Our mission is to move Alexa from reacting to commands toward understanding a household's rhythms and acting helpfully on the customer's behalf, reliably and at scale. Because no two homes are alike, we embrace that diversity rather than paper over it. We value research-grounded decisions, hands-on work, and long-horizon bets. Scientists own their areas end-to-end, mentor one another, publish, and partner closely with engineering and product.

Basic qualifications

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

Preferred qualifications

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

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 - 195,900.00 - 327,200.00 CAD annually

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Skills and AI tools this role asks for

TensorFlowscikit-learn

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What's a project where you used TensorFlow hands-on?
Walk me through how you've used scikit-learn in your day-to-day work.
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?

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