AmazonPosted 1mo ago

Applied Scientist II, Alexa International Tech

Applied Scientist II, Alexa International Tech at Amazon scores 100 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.

IN, KA, Bengalurumidfull-time

AI in this role

llm-integrationfine-tuningai-evaluationspeech

Alexa International is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs), ASR, TTS, and Speech to Speech models, requiring strong deep learning and generative models knowledge. You will contribute to developing novel solutions and deliver high-quality results that impact Alexa's international products and services.

Key job responsibilities
As an Applied Scientist with the Alexa International team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with Large Language Models (LLMs), ASR, TTS, and Speech to Speech model. Your work will directly impact our international customers in the form of products and services that make use of digital assistant technology. You will leverage Amazon's heterogeneous data sources, unique and diverse international customer nuances and large-scale computing resources to accelerate advances in text, voice, and vision domains in a multimodal setup. The ideal candidate possesses a solid understanding of machine learning, natural language understanding, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environments to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and collaborate effectively with cross-functional teams.

A day in the life
* Analyze, understand, and model customer behavior and the customer experience based on large-scale data.
* Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants.
* Drive research in ASR, TTS, and Speech-to-Speech (S2S) model training and fine-tuning
* Advance multilingual speech recognition and synthesis using LLM-based architectures
* Fine-tune/post-train LLMs using techniques like SFT, DPO, RLHF, and RLAIF.
* Collaborate with partner teams on evaluation frameworks and post-training methodologies.
* Communicate solutions clearly to partners and stakeholders.
* Contribute to the scientific community through publications and community engagement.

Basic qualifications

- 3+ years of building machine learning 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 in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience programming in Java, C++, Python or related language
- Deep expertise in ASR/TTS architectures, end-to-end speech models, and LLM integration with speech systems

Preferred qualifications

- Experience using Unix/Linux
- Experience building machine learning models or developing algorithms for business application
- Deep expertise in state-of-the-art LLM architectures, training, evaluation, and post-training techniques (SFT, DPO, RLHF, RLAIF)

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.

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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

Llm IntegrationFine TuningAI EvaluationSpeech

Questions you could be asked

How have you integrated a large language model into a production application?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you decide that one model's output is better than another's for a given task?
What have you built with speech recognition or text-to-speech, and where did it break?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: Llm Integration, Fine Tuning, AI Evaluation, and Speech. 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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