Applied Scientist, Alexa Connections

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

Job summary

Amazon Development Centre Canada ULC in Toronto seeks an Applied Scientist to advance language intelligence for a trusted communications agent. You will research, design, train and deploy NLP models that understand intent, context and relationships, and work with product teams to ship scalable, privacy-first features.

You will collaborate to refine retrieval-augmented generation and evaluation frameworks, bringing models from research to production for millions of users.

Qualifications

• PhD or Master’s degree with 4+ years of ML/model development experience.
• 2+ years of programming in Java, C++, Python or related language.
• Experience building and deploying NLP models in production (NER, entity linking, intent detection, language understanding).

Responsibilities

• Research, develop and deploy language intelligence powering communications using LLMs.
• Reframe NLP tasks as LLM-native problems via instruction-tuning, in-context learning and structured generation.
• Ship LLM-powered features at scale and define evaluation frameworks for quality, faithfulness and latency.

Skills

Java

C++

Python

NLP

LLMs

Entity Linking

Education

Master's degree

PhD

Job description

Description

Alexa Connections is on a mission to become the world's most trusted communication agent, spanning calls, text, email, and the ever-expanding surfaces where people connect. We're building intelligence that keeps customers connected effortlessly while putting their trust and privacy first. As an Applied Scientist, you'll help build the smartest communications agent for people, an agent that understands intent, context, and relationships, and that acts on a customer's behalf to make every interaction feel effortless and genuinely helpful

Key job responsibilities

You'll research, develop, and deploy the language intelligence that powers how people communicate, using and adapting large language models to understand intent, context, and the entities that matter in a conversation, the people, contacts, events, dates, and topics that tell the agent who and what a message is about. In practice, that means reframing classic NLP and named entity recognition as LLM-native tasks through instruction-tuning, in-context learning, and structured generation, and building agentic capabilities that reason over multi-turn conversations and act on them, from summarization and smart replies to coreference and context resolution grounded in a customer's relationships and history. You'll fine-tune, align, and optimize foundation models for the messiness of real communication data, informal text, code-switching, misspellings, transcribed speech, and multilingual content, and ground their outputs in customer context using retrieval-augmented generation and personalization so responses stay relevant and reliable. Throughout, you'll take models from research to production at scale, partnering with engineering and product teams to ship LLM-powered features used by millions every day, and define the evaluation frameworks that measure quality, faithfulness, latency, and customer impact while keeping trust and privacy first.

Basic Qualifications

• PhD, or Master's degree and 4+ years of building machine learning models or developing algorithms for business application experience
• 2+ years of programming in Java, C++, Python or related language experience
• Experience building and deploying NLP models in production, such as named entity recognition, entity linking, intent detection, or language understanding
  

Preferred Qualifications

• PhD in computer science, machine learning, engineering, or related fields
• Knowledge of standard speech and machine learning techniques
• Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)
• Experience applying large language models to NLP tasks, including instruction-tuning, in-context learning, or retrieval-augmented generation (RAG)
• Experience with conversational AI, dialogue systems, or communication/messaging applications
• Experience with multilingual NLP, coreference resolution, summarization, or working with noisy real-world text (transcribed speech, informal messaging
  

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

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

Company

Amazon Development Centre Canada ULC

Job ID: A10557195