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Senior Applied Scientist, APEX

Amazon.com Inc

  • Bellevue, WA
  • 3 days ago

    Highlights

    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. PhD/MS in Computer Science, Electrical Engineering, Machine Learning, Natural Language Processing, or a related technical field, OR Master"s degree with 5+ years of relevant industry experience.

    Numbers & Facts

    LocationBellevue, WA
    IndustryRetail
    Company Size10,000 employees or more
    Year Founded1994
    Websitehttp://Amazon.com/militaryroles

    Description

    Alexa AI is looking for a Senior 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.

    As a Senior Applied Scientist, you are a strong technical contributor who independently drives complex projects from ideation to production. You design and run rigorous experiments, develop novel approaches to challenging problems, and deliver high-quality models and systems at scale. Your work is characterized by scientific rigor, engineering excellence, and a focus on measurable customer impact.

    You collaborate effectively across teams, contribute to scientific discussions and reviews, and help elevate the technical bar within the organization. You proactively identify opportunities, propose solutions, and influence technical direction within your project area.

    Basic Qualifications

    • PhD/MS in Computer Science, Electrical Engineering, Machine Learning, Natural Language Processing, or a related technical field, OR Master"s degree with 5+ years of relevant industry experience
    • 3+ years of hands-on experience in applied machine learning, predictive modeling, or NLP
    • Strong programming skills in Python or a related language
    • Experience with deep learning frameworks (e.g., PyTorch, TensorFlow)
    • Track record of delivering ML/NLP solutions from research to production
    • Experience working with large language models (training, fine-tuning, or evaluation)

    Preferred Qualifications

    • 5+ years of relevant industry or academic research experience
    • Experience with LLM alignment techniques (RLHF, DPO, constitutional AI)
    • Experience with agentic AI systems, including planning, tool use, and orchestration
    • Experience with distributed training and large-scale model optimization
    • Peer-reviewed publications at top-tier venues (e.g., NeurIPS, ICML, ACL, EMNLP, ICLR)
    • Strong communication skills with the ability to present complex technical concepts to diverse audiences
    • Experience mentoring junior scientists or engineers

    Key job responsibilities

    • Design, implement, and evaluate novel approaches to LLM fine-tuning, alignment (RLHF, DPO), and distillation for production deployment
    • Develop and improve agentic systems - including multi-step reasoning, tool use, planning, and orchestration - that work reliably at scale
    • Build 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
    • Analyze large-scale experimental results, identify patterns, and iterate rapidly on model improvements
    • Publish results at top-tier venues and contribute to Amazon"s presence in the broader research community
    • Mentor junior scientists and contribute to hiring efforts

    About the team

    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 from human feedback and verifiable rewards, 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.

    About Company

    At Amazon, we don’t wait for the next big idea to present itself. We envision the shape of impossible things and then we boldly make them reality. So far, this mindset has helped us achieve some incredible things. Let’s build new systems, challenge the status quo, and design the world we want to live in. We believe the work you do here will be the best work of your life.

    Wherever you are in your career exploration, Amazon likely has an opportunity for you. Our research scientists and engineers shape the future of natural language understanding with Alexa. Fulfillment center associates around the globe send customer orders from our warehouses to doorsteps. Product managers set feature requirements, strategy, and marketing messages for brand new customer experiences. And as we grow, we’ll add jobs that haven’t been invented yet.

    It’s Always Day 1
    At Amazon, it’s always “Day 1.” Now, what does this mean and why does it matter? It means that our approach remains the same as it was on Amazon’s very first day – to make smart, fast decisions, stay nimble, invent, and stay focused on delighting our customers. In our 2016 shareholder letter, Amazon CEO Jeff Bezos shared his thoughts on how to keep up a Day 1 company mindset. “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,” he wrote. “A customer-obsessed culture best creates the conditions where all of that can happen.” You can read the full letter here

    Our Leadership Principles
    Our Leadership Principles help us keep a Day 1 mentality. They aren’t just a pretty inspirational wall hanging. Amazonians use them, every day, whether they’re discussing ideas for new projects, deciding on the best solution for a customer’s problem, or interviewing candidates. To read through our Leadership Principles from Customer Obsession to Bias for Action, visit https://www.amazon.jobs/principles

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