Applied Scientist II, Alexa Daily Essentials Science and Analytics

Amazon; British Columbia; On-site; CAD 149,000 - 249,000; Full time

Benefits offered by this job

Health insurance

RRSP

DPSP

Paid time off

Job summary

Amazon Development Centre Canada ULC in Vancouver, BC is seeking an Applied Scientist II to design agentic AI systems that autonomously detect, diagnose, and act on signals across Alexa's product surface. You will build the evaluation science that validates these systems, prototype novel AI-powered features, and apply reinforcement learning to improve agent behavior through outcome signals.

You will own science end-to-end—from rapid prototyping through production deployment—working directly with

Qualifications

• PhD or Master’s degree in CS/CE/ML or related field with 4+ years experience
• 3+ years building ML models for business application
• Experience in patents or publications at top-tier conferences or journals
• Experience programming in Java, C++, Python or related language
• Experience in machine learning, reinforcement learning, deep learning, NLP or related applications

Responsibilities

• Design and implement agentic AI systems with multi-step reasoning and tool use
• Apply reinforcement learning to improve routing, decision-making and task resolution quality
• Build evaluation and benchmarking frameworks and automated test suites
• Prototype and develop AI-powered features from concept through production
• Build RAG pipelines and knowledge retrieval systems for grounded reasoning
• Design and execute end-to-end experiments influencing product roadmaps
• Communicate findings to both technical and non-technical audiences

Skills

Java

C++

Python

NLP

Reinforcement Learning

Deep Learning

Computer Vision

Education

PhD or Master in CS/CE/ML

Tools

Java

C++

Python

Job description

On the Alexa Daily Essentials Science and Analytics team, we build intelligence systems that power AI-driven experiences for customer-facing features used by hundreds of millions of people daily. Our team applies foundation models in novel ways — designing agentic systems that reason, plan, and act autonomously over complex data, and rigorously evaluating their performance so that every architecture decision is grounded in measured quality. We're not actively training foundation models. We're pushing the frontier of how they're applied — through agentic architectures, reinforcement learning, retrieval-augmented generation, and evaluation science.

As an Applied Scientist II, you will design agentic AI systems that autonomously detect, diagnose, and act on signals across Alexa's product surface. You'll build the evaluation science that validates these systems, prototype novel AI-powered features that create new value for customers, and apply reinforcement learning to improve agent behavior through outcome signals. You'll own science end-to-end — from rapid prototyping through production deployment — working directly with engineers and product leaders to ship systems that impact how millions of customers interact with Alexa daily. This is a role for a scientist who brings rigor to evaluation, creativity to invention, and pragmatism to delivery.

Key job responsibilities

• Design and implement agentic AI systems — multi-step reasoning, tool use,planning, and orchestration — that autonomously surface intelligence and takeaction over complex, evolving data
• Apply reinforcement learning techniques to improve agent routing,decision-making, and task resolution quality from outcome signals
• Build evaluation and benchmarking frameworks — automated test suites,LLM-as-judge pipelines, competitive benchmarks, and regression detectionacross model versions and prompt strategies
• Prototype and develop novel AI-powered features — information extraction,proactive recommendations, multi-agent collaboration — from concept throughproduction launch
• Build RAG pipelines and knowledge retrieval systems that ground agentreasoning in trusted data
• Design and execute experiments end-to-end — hypothesis generation, causalanalysis, and results interpretation that directly shape product roadmaps
• Communicate findings to technical and non-technical audiences — influencingarchitecture decisions, model selection, and product strategy throughrigorous evaluation

A day in the life

You might start by analyzing reward signal distributions from your latest RLexperiment on agent routing — identifying where the policy improved and whereexploration is still needed. Mid-morning, you prototype a new cross-featureexperience, testing whether extraction models can reliably surface actionableitems from customer content. After lunch, you run your eval suite against a newmodel version — comparing benchmark scores across reasoning depth, tool-usereliability, and output consistency to inform an architecture decision. Later,you pair with an engineer to add a new capability to an agent's tool registry,then validate it doesn't degrade downstream quality. You end the day designingan A/B test for a proactive feature your team is launching.

About The Team

We’re a small, high-ownership team building agentic intelligence systems forAlexa — one of the world's most widely used AI products, reaching hundreds ofmillions of customers across devices, languages, and contexts. We applyfoundation models in novel ways: systems that reason over temporal data, learnfrom outcomes through reinforcement, autonomously detect regressions, andsurface insights that previously required weeks of manual analysis. Ourscientists prototype new features, benchmark rigorously, and ship from researchthrough production. If you want to build at the frontier of applied AI — agenticsystems, reinforcement learning, evaluation science — with real users and realimpact at Alexa scale, this is the team.

Basic Qualifications

• 3+ years of building machine learning models for business applicationexperience
• PhD, or Master's degree and 4+ years of CS, CE, ML or related fieldexperience
• Experience in patents or publications at top-tier peer-reviewed conferencesor journals
• Experience programming in Java, C++, Python or related language
• Experience with one of the following areas: machine learning technologies,Reinforcement Learning, Deep Learning, Computer Vision, Natural LanguageProcessing (NLP) or related applications

Preferred Qualifications

• Experience in professional software development
• Experience creating novel algorithms and advancing the state of the art
• Experience designing or working with agentic AI systems (tool-use, multi-stepreasoning, RAG, multi-agent orchestration)
• Experience building automated evaluation frameworks or LLM-as-judge systems

Amazon is an equal opportunity employer and does not discriminate on the basisof protected veteran status, disability, or other legally protected status.Our inclusive culture empowers Amazonians to deliver the best results for ourcustomers. If you have a disability and need a workplace accommodation oradjustment during the application and hiring process, including support for theinterview 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 yourRecruiting Partner.

The base salary range for this position is listed below. As a total compensationcompany, Amazon's package may include other elements such as sign-on paymentsand restricted stock units (RSUs). Final compensation will be determined basedon factors including experience, qualifications, and location. Amazon offerscomprehensive 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 resourcesto 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

Company - Amazon Development Centre Canada ULC

Job ID: A10553157