Applied Scientist II - Amz9971140

Amazon Amazon · Big Tech · Seattle, WA · Corporate Operations

Applied Scientist II at Amazon Web Services focusing on designing, developing, evaluating, and deploying data-driven models and analytical solutions for machine learning and natural language applications. The role involves researching and implementing novel ML and statistical approaches, including deep learning architectures and Bayesian inference, for formal verification, program analysis, and code generation. Key responsibilities include developing automated reasoning techniques for generative AI and agentic coding systems, ensuring the safety and alignment of AI agents, and applying formal guarantees to LLM outputs. The position also requires conducting original research at the intersection of ML and formal methods, publishing findings, and building production-grade automated reasoning tools and ML pipelines for AWS infrastructure. Mentoring junior scientists is also a component of the role.

What you'd actually do

  1. Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications.
  2. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering.
  3. Routinely build and deploy ML models on available data.
  4. Research and implement novel ML and statistical approaches to add value to the business.
  5. Mentor junior engineers and scientists.

Skills

Required

  • Master's degree or foreign equivalent degree in Computer Science, Machine Learning, Statistics, or a related field and one year of research or work experience in the job offered or as a Research Scientist, Research Assistant, Software Engineer, or a related occupation.
  • programming in Java, C++, Python, or equivalent programming language.

Nice to have

  • Bachelor's degree or foreign equivalent degree in Computer Science, Machine Learning, Statistics, or a related field and five years of progressive post baccalaureate research or work experience in the job offered or a related occupation.

What the JD emphasized

  • design, development, evaluation, deployment and updating of data-driven models
  • ML and/or NL applications
  • statistical modeling techniques
  • ML techniques
  • build and deploy ML models
  • novel ML and statistical approaches
  • automated reasoning — including constraint solving, model checking, static analysis, and theorem proving
  • cloud computing systems and generative AI applications
  • deep learning architectures (e.g., graph neural networks, transformers, recurrent models)
  • statistical modeling techniques (e.g., Bayesian inference, probabilistic programming)
  • formal verification, program analysis, and code generation
  • automated reasoning techniques for generative AI and agentic coding systems
  • verifying the correctness of AI-generated code
  • safety and alignment of autonomous software agents
  • formal guarantees to large language model (LLM) outputs
  • neuro-symbolic reasoning, program synthesis, interactive and automated theorem proving, abstract interpretation, scalable verification techniques, and formal methods for AI safety
  • ML with symbolic and logical reasoning
  • automated verification tools used across AWS services
  • access control policy analysis, network configuration verification, resource compliance checking, and system reliability assurance
  • optimization methods — including linear and integer programming, convex optimization, and heuristic search
  • constraint satisfaction, resource allocation, and scheduling problems
  • production-grade automated reasoning tools and ML pipelines for AWS infrastructure
  • rigorous statistical methods
  • improve performance at scale

Other signals

  • design, development, evaluation, deployment and updating of data-driven models
  • ML and/or NL applications
  • statistical modeling techniques
  • ML techniques
  • build and deploy ML models
  • novel ML and statistical approaches
  • automated reasoning — including constraint solving, model checking, static analysis, and theorem proving
  • cloud computing systems and generative AI applications
  • deep learning architectures (e.g., graph neural networks, transformers, recurrent models)
  • statistical modeling techniques (e.g., Bayesian inference, probabilistic programming)
  • formal verification, program analysis, and code generation
  • automated reasoning techniques for generative AI and agentic coding systems
  • verifying the correctness of AI-generated code
  • safety and alignment of autonomous software agents
  • formal guarantees to large language model (LLM) outputs
  • neuro-symbolic reasoning, program synthesis, interactive and automated theorem proving, abstract interpretation, scalable verification techniques, and formal methods for AI safety
  • ML with symbolic and logical reasoning
  • automated verification tools used across AWS services
  • access control policy analysis, network configuration verification, resource compliance checking, and system reliability assurance
  • optimization methods — including linear and integer programming, convex optimization, and heuristic search
  • constraint satisfaction, resource allocation, and scheduling problems
  • production-grade automated reasoning tools and ML pipelines for AWS infrastructure
  • rigorous statistical methods
  • improve performance at scale