Applied Scientist - Intelligent Automation & AI Research

Amazon Web Services (AWS); New York (NY); On-site; USD 172,000 - 223,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon Web Services, Inc. is seeking researchers to build next-generation agentic AI models for intelligent automation in a fast-paced AWS environment.

The role involves partnering with technology and business teams to tackle real-world problems, leveraging vast data and computational resources, and publishing findings at peer-reviewed venues. Qualified candidates will have strong backgrounds in autonomous agents, planning, RL, and large multimodal models.

Qualifications

• 3+ years of building models for business applications.
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience.
• Experience in patents or publications at top-tier peer-reviewed conferences or journals.
• Experience programming in Java, C++, Python or related language.
• Experience in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.

Responsibilities

• Define and implement new automated reasoning features using scalable approaches.
• Apply software engineering best practices to ensure high quality deliverables.
• Work in an agile, startup-like development environment.
• Deliver high-quality scientific artifacts and methods.
• Collaborate with technology and business teams to guide decisions with research insights.

Skills

Model building

Programming languages

Algorithms

Distributed computing

Unix/Linux

Education

PhD

MS in CS/CE/ML

Tools

Unix/Linux

Job description

Amazon Web Services, Inc. is seeking researchers to build next-generation agentic AI models for intelligent automation in a fast-paced AWS environment.

The role involves partnering with technology and business teams to tackle real-world problems, leveraging vast data and computational resources, and publishing findings at peer-reviewed venues. Qualified candidates will have strong backgrounds in autonomous agents, planning, RL, and large multimodal models.