AmazonPosted 1d ago

Applied Scientist, Alexa Connections

Applied Scientist, Alexa Connections 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, Vancouverseniorfull-timeCAD 149k-CAD 249k

AI in this role

Researches and develops LLM-powered language intelligence and agentic communication features for Alexa.

pythonjavacllmsrag

ragnlpspeechmachine-learningapplied-sciencemodel-fine-tuningretrieval-augmented-generation

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

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RagNlpSpeechMachine LearningApplied ScienceModel Fine TuningRetrieval Augmented GenerationPython

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
What NLP problem have you worked on, and how did you measure whether it actually worked?
What have you built with speech recognition or text-to-speech, and where did it break?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where applied science was part of your work. What did you do?

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List these exact terms on your resume: Rag, Nlp, Speech, Machine Learning, and Applied Science. 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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