AmazonPosted 3mo ago

Applied Scientist, AICE - AI Center of Excellence

Applied Scientist, AICE - AI Center of Excellence at Amazon scores 95 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, Vancouverfull-timeCAD 149k-CAD 249k

AI in this role

ai-evaluationai-safety

The AI Center of Excellence (AICE) builds AI primitives that power system-intrinsic intelligence and Trusted Intelligent Knowledge Infrastructure for both AI-as-a-Consumer and human users. We develop AI capabilities independently and in partnership with product-owning teams to evolve Amazon's AI-driven operations and productivity. Our impact is enterprise-broad and global.

We are looking for an Applied Scientist who can identify high-impact problems, design rigorous experiments, and translate research into hardened, reusable primitives ready for broad consumption across the enterprise.

Key job responsibilities
* Research, design, and develop AI/ML primitives - defining problem spaces, formulating approaches, running experiments, and validating outcomes with scientific rigor
* Translate research findings into production-ready primitives, working closely with machine learning engineers to harden and scale solutions
* Drive the scientific direction of AICE primitives, identifying opportunities where novel approaches can unlock step-function improvements
* Design and execute experiments to validate primitive performance, robustness, and generalizability before broad consumption by partner teams
* Collaborate with product-owning teams to understand their problem spaces and ensure AICE primitives deliver measurable value when integrated
* Contribute to AICE's mission of driving AI transformation through best practices in applied research, evaluation methodology, and responsible AI within your area of expertise
* Publish findings, share learnings, and raise the scientific bar across the team

A day in the life
Every day brings new challenges. You might be deep in experimentation - designing evaluations to stress-test a new primitive's robustness. The next day, you could be analyzing results and iterating on model architectures. Later in the week, you may collaborate with partners to understand their domain and shape a primitive that fits their product needs. Other days, you'll work alongside our MLEs to harden a validated approach for broader consumption.

No two weeks look the same. You'll move fluidly between research and applied problem-solving - always with the goal of building AI primitives that are scientifically sound, trusted, and impactful at enterprise scale.

About the team
AICE is a team of scientists and machine learning engineers building the AI primitives that power Amazon's intelligent systems. We develop and harden reusable capabilities for broad consumption by product teams across the enterprise - and partner with those teams to integrate them.

Basic qualifications

- PhD in Computer Science (Machine Learning, AI, Statistics, or equivalent)
- Experience with programming languages such as Python, Java, C++
- Ability to work across the research-to-production spectrum - from formulating hypotheses to validating primitives in production environments
- Familiarity with traditional software development practices with the ability to also leverage AI-assisted development tools effectively
- Experience designing and running experiments, A/B tests, or rigorous offline evaluations
- Track record of translating research into applied solutions that operate at scale
- Strong foundation in statistical methods, experimental design, and model evaluation

Preferred qualifications

- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience building reusable ML/AI components, frameworks, or primitives consumed by other teams
- Experience in information retrieval, knowledge representation, or agentic AI systems
- Demonstrated ability to collaborate with engineering teams to harden research prototypes for production

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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Skills and AI tools this role asks for

AI EvaluationAI Safety

Questions you could be asked

How do you decide that one model's output is better than another's for a given task?
How do you think about the risk of an AI system in this kind of role failing silently?
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?

Adapt your resume

List these exact terms on your resume: AI Evaluation and AI Safety. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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