Applied Scientist, Conversational Assistant Modeling and Learning

About the role

Alexa AI is building the science and technology behind Alexa+, Amazon's next-generation conversational assistant. Our team works at the intersection of large language models, reinforcement learning, agentic architectures, and multilingual/multimodal understanding. We operate at massive scale - our models serve customers across dozens of languages and device types. If you want to push the frontier of conversational AI and see your work used by people every day, come join us.

Requirements

• Master's degree or above in computer science, machine learning, engineering, or related fields
• 3+ years of programming in Java, C++, Python or related language experience
• 1+ years experience with distributed training frameworks such as VeRL, Megatron, FSDP, DeepSpeed, Ray, or similar systems, and inference engines such as vLLM, TensorRT-LLM, Triton, SGLang, TGI.
• 3+ years' experience with modeling languages and tools like PyTorch / TensorFlow, R, scikit-learn, numpy, scipy, etc.
• Solid ML background and familiar with NLU, NLG, and LLM training and evaluation.
• PhD in computer science, machine learning, engineering, or related fields
• 3+ years experience with distributed training frameworks such as verl, Megatron, FSDP, DeepSpeed, Ray, or similar systems, and inference engines such as vLLM, TensorRT-LLM, Triton, SGLang, TGI.
• Publications at peer-reviewed NLP/ML conferences (e.g. ACL, EMNLP, NAACL, NeurIPS, ICLR, ICML, AAAI, etc.)
• Scientific thinking and the ability to invent, a track record of thought leadership and contributions that have advanced the field.
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
• USA, WA, Bellevue - 142,800.00 - 193,200.00 USD annually

Additional Information

Alexa AI is looking for an Applied Scientist to build Alexa+, Amazon's LLM-powered conversational assistant. You will work on key initiatives spanning large language model fine-tuning, alignment, agentic reasoning, and evaluation - directly shaping the experience for hundreds of millions of customers worldwide. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Scientist, you will own the design and development of end-to-end systems. You'll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with other scientists and engineers to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Key job responsibilities * Improve the efficiency of LLM, VLM, and agent training and evaluation pipelines, including distributed training, inference serving, data loading, checkpointing, memory usage, and GPU utilization. * Design, implement, and evaluate novel approaches to LLM fine-tuning, alignment (RLHF, DPO), and distillation for production deployment * Architect agentic systems - multi-step reasoning, tool use, planning, and orchestration * Develop evaluation frameworks and methodologies that go beyond standard benchmarks to capture real-world conversational quality * Translate research advances into customer-facing products, working closely with engineering, product, and cross-functional science teams * Publish results at top-tier venues and represent Amazon in the broader research community

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