Applied Scientist, Sales AI

Amazon is hiring an Applied Scientist, Sales AI in Toronto, Canada. It pays CAD 149k-CAD 249k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

Level rates this applied scientist role Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include RAG, AI agents and NLP.
Level classifies it as a mid-level role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

ragai-agentsnlp

Are you interested in shaping the future of Advertising and B2B Sales? We are a growing team with an exciting AI-first charter and need your passion, innovative thinking, and creativity to help take our products to new heights.

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products are strategically important to our businesses driving long term growth. We break fresh ground in product and technical innovations every day!

Within the Advertising Sales organization, we are building a central AI/ML team and are seeking top Applied Science talent to help us build new, science-backed services that drive success for our customers. Our goal is to transform the way account teams operate by creating AI agents that help optimize their end-to-end workflows, and developing
actionable insights and recommendations they can share with their advertising accounts

As an Applied Scientist on the team with a specific focus on creating autonomous AI agents that can operate accurately at large scale, you will bring deep expertise in Natural Language Processing (inc. tokenization, syntactic parsing, named entity recognition (NER), sentiment analysis, text classification), Large Language Models (inc. foundation model fundamentals, post-training, reward modeling, RAG, transformer architecture), Deep Learning, Reinforcement Learning and/or Recommender Systems. You have the scientific and technical skills to build and refine models that can be implemented in production and you continuously measure the performance of your system to drive continuous improvements. You will contribute to chart new courses with our ad sales support technologies, and you have the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers. You will be part of a team of fellow scientists and engineers taking on iterative approaches to tackle big, long-term problems. You are fluently able to leverage the latest Generative AI systems and services to accelerate and improve your work while maintaining high quality in your work outputs.

Key job responsibilities
Scientific Modeling
- Conceptualize and lead state-of-the-art research on new Reinforcement Learning, Deep Learning, NLP, LLM, (Generative) Artificial Intelligence and Recommender System solutions to create AI agents and optimize all aspects of the Ad Sales business
- Lead the technical approach for the design and implementation of successful models and algorithms in support of expert cross-functional teams delivering on demanding projects
- Run regular A/B experiments, gather data, and perform statistical analysis
- Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving
- Publish scientific findings in reports and papers that can be shared internally and externally

Product Development Support
- Partner with software engineering and product management teams to support product and service development, define success metrics and measurement approaches, and help drive adoption of innovative new features for our services.
- Lead requirements gathering sessions with product teams and business stakeholders
- Maintain scientific documentation and knowledge for product initiatives

Collaboration & Communication
- Work closely with software engineers to deliver end-to-end solutions into production
- Translate complex scientific findings into actionable business recommendations for stakeholders and senior management
- Provide clear, compelling reports and presentations on a regular basis with respect to your models and services
- Communicate with internal teams to showcase results and identify best practices.

About the team
Sales AI is a central science and engineering organization within Amazon Advertising Sales that powers selling motions and account team workflows via state-of-the-art of AI/ML services. Sales AI is investing in a range of sales intelligence models, including the development of advertiser insights, recommendations and Generative AI-powered applications throughout account team workflows.

Basic qualifications

- 3+ years of building 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 in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred qualifications

- Experience using Unix/Linux
- Experience in professional software development
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience programming with at least one modern language such as Java, C++, or C# including object-oriented design

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, ON, Toronto - 149,300.00 - 249,300.00 CAD annually

How we rate this

Applied Scientist, Sales AI at Amazon rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Applied Scientist, Sales AI is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is CAD 149k-CAD 249k a year.
The role is based in CA, ON, Toronto.
The job was posted on February 24, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsNLP

Also common in applied scientist jobs at this level

TensorFlow Spark AWS Computer vision scikit-learn PyTorch

Pay

Typical applied scientist job: $167k

Use the typical applied scientist figure as a reference when pay comes up. See applied scientist jobs

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide when an AI agent can act on its own versus asking for approval first?
What NLP problem have you worked on, and how did you measure whether it actually worked?
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: RAG, AI agents, and NLP. 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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