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
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
