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Added 2 hours ago
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PhD Student: wind-driven hail impacts on roofing, façade, and building-integrated photovoltaic (BIPV)
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- Full-time
Apply programming and data-analysis tools such as Python, MATLAB, and/or C++ for modelling, simulation, and statistical analysis.
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Amazon Science

Applied Scientist II, Alexa Daily Essentials Science and Analytics

Vancouver, BC, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Oct 18, 2026
Posting language: English
Posted: Sep 18, 2026

Original job description

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 take action 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 detection across model versions and prompt strategies
Prototype and develop novel AI-powered features - information extraction, proactive recommendations, multi-agent collaboration - from concept through production launch
Build RAG pipelines and knowledge retrieval systems that ground agent reasoning in trusted data
Design and execute experiments end-to-end - hypothesis generation, causal analysis, and results interpretation that directly shape product roadmaps
Communicate findings to technical and non-technical audiences - influencing architecture decisions, model selection, and product strategy through rigorous evaluation

A day in the life

You might start by analyzing reward signal distributions from your latest RL experiment on agent routing - identifying where the policy improved and where exploration is still needed. Mid-morning, you prototype a new cross-feature experience, testing whether extraction models can reliably surface actionable items from customer content. After lunch, you run your eval suite against a new model version - comparing benchmark scores across reasoning depth, tool-use reliability, 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 designing an 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 for Alexa - one of the world's most widely used AI products, reaching hundreds of millions of customers across devices, languages, and contexts. We apply foundation models in novel ways: systems that reason over temporal data, learn from outcomes through reinforcement, autonomously detect regressions, and surface insights that previously required weeks of manual analysis. Our scientists prototype new features, benchmark rigorously, and ship from research through production. If you want to build at the frontier of applied AI - agentic systems, reinforcement learning, evaluation science - with real users and real impact at Alexa scale, this is the team.

Basic Qualifications

3+ years of building machine learning models for business application experience
PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
Experience in patents or publications at top-tier peer-reviewed conferences or 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 Language Processing (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-step reasoning, 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 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

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More about this job

Responsibilities

Design and deploy agentic AI systems, retrieval pipelines, and reinforcement learning approaches that detect signals, make decisions, and take action across Alexa products. Develop rigorous evaluation and benchmarking methods, prototype customer-facing AI features, and run experiments that inform product and architecture decisions.

Requirements

Requires a PhD or a master's degree with at least four years of relevant experience, plus three or more years building machine learning models for business applications. Candidates must have programming experience in Python, Java, C++, or a related language; experience in a relevant machine learning field; and patents or publications at top-tier peer-reviewed venues.

Skills

Agentic AI Systems
Reinforcement Learning
Machine Learning
Deep Learning
Natural Language Processing
Python
Java
C++
Retrieval-Augmented Generation
Multi-Step Reasoning
Tool Use and Orchestration
Evaluation Frameworks
LLM-as-Judge Systems
Experiment Design
Causal Analysis
Software Development

Benefits

Health Insurance
Dental Insurance
Vision Insurance
Prescription Coverage
Basic Life Insurance
Accidental Death and Dismemberment Insurance
Deferred Profit Sharing Plan
Paid Time Off
Sign-On Payments
Restricted Stock Units

Visa sponsorship

Not detected in the job text

Keywords

Alexa
Foundation Models
Agentic AI
Reinforcement Learning
Retrieval-Augmented Generation
Machine Learning
Deep Learning
Natural Language Processing
Python
Java
C++
Multi-Step Reasoning
Tool Use
Planning
Orchestration
Multi-Agent Collaboration
Knowledge Retrieval
Evaluation Science
Automated Test Suites
LLM-as-Judge
Benchmarking
Regression Detection
Information Extraction
Proactive Recommendations
Causal Analysis
A/B Testing
Agent Routing
Outcome Signals
Production Deployment
Peer-Reviewed Publications

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