Applied Scientist, Amazon

• PhD in computer science, machine learning, engineering, or related fields
• Knowledge of at least one programming language such as Java, C#, JavaScript, Python, Ruby or Perl
• Experience in designing experiments and statistical analysis of results
• Hands-on experience building, training, and evaluating LLMs

Desirable

• As an Applied Scientist II in the Alexa Conversational Modelling Intelligence team within Alexa AI, you will drive model post-training for Large Language Models that power Alexa+
• You'll adopt and adapt state-of-the-art techniques — including supervised fine-tuning, reinforcement learning, preference optimization, and knowledge distillation — running rigorous experiments and translating findings into production-ready solutions that directly improve the customer experience for millions of users worldwide
• You will own the full model development cycle from data curation through training, evaluation, and deployment
• Your day-to-day will involve developing evaluation methods and metrics, diagnosing model defects, optimizing model training pipelines, and iterating on recipes to move concrete quality and efficiency benchmarks
• You'll write clean, reproducible code, contribute to shared tooling, and collaborate closely with scientists and engineers to bring models from experimentation to scale
• You are technically curious, experiment-driven, and motivated by real customer impact
• You are an expert in LLM post-training
• You will also advance the state of the art by publishing at top-tier NLP/ML conferences (ACL, EMNLP, NeurIPS, ICML, ICLR) — contributing to the broader research community while grounding your work in measurable outcomes
• Own the full model development cycle — from data curation through training, evaluation, and deployment
• Develop and apply post-training techniques: supervised fine-tuning, reinforcement learning, preference optimization, and knowledge distillation
• Build evaluation methods and metrics, and diagnose model defects to target the highest-impact improvements
• Optimize model training pipelines and iterate on recipes to move concrete quality and efficiency benchmarks
• Write high-quality documentation on methods and experiment outcomes, and communicate findings clearly to stakeholders
• Post-training is one of the most active frontiers in LLMs right now
• The field has moved from scaling pretraining to getting more out of models afterward through RL, reasoning recipes, and preference optimization
• You'll work on these techniques directly, on a product used by millions of customers every day
• A typical day: review overnight training runs and dashboards, dig into model defects to form hypotheses, then curate data and iterate on a recipe, improving shared tooling along the way
• You'll sync with scientists and engineers to unblock the path to production, and write up your findings for stakeholders
• It's fast-moving — a good idea can reach millions of customers within weeks
• The Alexa Conversational Modelling Intelligence team builds industry-leading LLM-based conversational technologies that customers love
• Our mission is to push the envelope in LLMs for Alexa to deliver the best-possible customer experience
• As an Applied Scientist, you'll contribute directly to that mission through model development and experimentation