Applied Scientist, Selling Partner Support Engagement

Amazon Amazon · Big Tech · Seattle, WA · Data Science

Research scientist role focused on building and improving AI agents for customer support, involving RL-based systems, preference learning, reward modeling, and policy optimization. The role emphasizes end-to-end ownership from research to production deployment, collaboration with engineering teams, and publishing research. It operates within an enterprise AI domain focused on scaling AI solutions for customer interactions.

What you'd actually do

  1. Own end-to-end research and development of RL-based agent improvement systems — from problem formulation through production deployment and impact measurement.
  2. Design novel approaches to preference learning, reward modeling, and policy optimization in the context of conversational agents operating over real-world tools and APIs.
  3. Build and maintain evaluation frameworks that measure agent quality across multiple dimensions: helpfulness, correctness, safety, and alignment with operational standards.
  4. Collaborate with a team of scientists that work on forefront of Natural Language Understanding, Optimization, Machine Learning and Statistics
  5. Partner with 10+ engineering teams to deploy models into production systems serving sellers worldwide.

Skills

Required

  • Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field
  • Experience programming in Java, C++, Python or related language
  • Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse

Nice to have

  • Experience implementing algorithms using both toolkits and self-developed code
  • Have publications at top-tier peer-reviewed conferences or journals
  • PhD in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields
  • Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)
  • Experience researching about machine learning, deep learning, NLP, computer vision, data science
  • Demonstrated experience leveraging generative AI tools to enhance workflow efficiency and productivity, with the ability to craft effective prompts and critically evaluate AI-generated outputs in a professional setting

What the JD emphasized

  • Own end-to-end research and development
  • production deployment
  • impact measurement
  • novel approaches
  • conversational agents operating over real-world tools and APIs
  • evaluation frameworks
  • agent quality
  • helpfulness, correctness, safety, and alignment
  • deploy models into production systems
  • publish research at top venues
  • novel techniques
  • prototype to production
  • deployed science solutions
  • mentorship of senior scientists
  • agentic solutions
  • development to evaluation
  • Agentic Systems
  • Knowledge Retrieval & Query Understanding
  • Content Intelligence & Automation
  • work backwards from business problems
  • gold-standard datasets
  • success metrics
  • guardrails
  • parallel experiments
  • compare approaches rigorously
  • ship the best Science models to production
  • publish at internal conferences and external venues
  • invest in research that compounds over multiple product cycles
  • high autonomy
  • own their domains end-to-end
  • problem framing through production deployment
  • speed over perfection
  • scientific rigor over polish
  • experimentation over debate

Other signals

  • AI agents
  • autonomous resolution
  • minimal human intervention
  • reason, remember, and adapt
  • end-to-end resolution
  • augmenting associates
  • agentic architectures
  • large-scale production systems
  • preference learning
  • reward modeling
  • policy optimization
  • conversational agents
  • real-world tools and APIs
  • evaluation frameworks
  • agent quality
  • helpfulness, correctness, safety, and alignment
  • Natural Language Understanding
  • Optimization
  • Machine Learning
  • Statistics
  • deploy models into production systems
  • publish research at top venues
  • novel techniques
  • rapid prototypes
  • AI-assisted coding tools
  • prototype to production
  • translate seller pain points into deployed science solutions
  • mentorship of senior scientists
  • agentic solutions
  • development to evaluation
  • Agentic Systems
  • Knowledge Retrieval & Query Understanding
  • Content Intelligence & Automation
  • work backwards from business problems
  • gold-standard datasets
  • success metrics
  • guardrails
  • parallel experiments
  • compare approaches rigorously
  • ship the best Science models to production
  • publish at internal conferences and external venues
  • invest in research that compounds over multiple product cycles
  • high autonomy
  • own their domains end-to-end
  • problem framing through production deployment
  • speed over perfection
  • scientific rigor over polish
  • experimentation over debate
  • inclusive learning culture
  • individual growth is a priority
  • mentorship, knowledge-sharing, and career-advancing resources