Member of Technical Staff - Infrastructure Engineer, Frontier AI & Robotics (FAR)

Amazon; Seattle (WA); On-site; USD 150,000 - 300,000; Full time

Job summary

Amazon’s Frontier AI & Robotics (FAR) team in Seattle is hiring a Member of Technical Staff, Infrastructure. You'll be responsible for designing and operating infrastructure that supports AI and robotics research. This role involves building scalable data pipelines, collaborating with researchers, and ensuring system reliability.

The ideal candidate will have over 5 years of experience in distributed systems, a Bachelor's in Computer Science, and strong programming skills in Python and cloud platforms.

Qualifications

• 5+ years of distributed systems experience.
• Proficiency in Python and at least one systems or backend programming language.
• Experience with cloud infrastructure platforms (AWS, GCP, or Azure).

Responsibilities

• Design and build scalable compute and data infrastructure.
• Lead large technical initiatives and shape architecture of FAR’s research platform.
• Optimize query performance and data availability.

Skills

Distributed systems experience

Proficiency in Python

Experience with cloud infrastructure platforms (AWS, GCP, or Azure)

Understanding of system reliability principles

Experience building data pipelines

Education

Bachelor's degree in Computer Science or a related field

Tools

Go

Java

C++

Job description

Overview

Amazon’s Frontier AI & Robotics (FAR) team is seeking a Member of Technical Staff, Infrastructure to build and scale the foundational systems that power our robotics research and development platform. In this role, you will design and operate the distributed infrastructure that enables our researchers and engineers to train foundation models, run large-scale experiments, and deploy intelligent robotic systems at Amazon scale.

Join the next revolution in robotics, where you’ll work alongside world-renowned AI pioneers to push the boundaries of what’s possible in robotic intelligence. As a Member of Technical Staff focused on Infrastructure, you’ll build the critical platform layer that accelerates every aspect of FAR’s research — from high-throughput data pipelines and experiment management systems to low-latency model serving and configuration delivery for robotic deployments.

This role is deeply technical and focuses on performance, scalability, and reliability at scale. You will design systems that support volumes of training data, operate with strict latency requirements, and provide the compute and data foundation that enables breakthrough research across FAR’s robotics ecosystem.

Responsibilities

• Design and build scalable compute and data infrastructure to support model training, inferencing, and eval for frontier AI/Robotics development
• Lead large technical initiatives and shape the architecture of FAR’s research platform infrastructure
• Develop tooling and frameworks that accelerate research workflows, including dataset management, visualization, and quality assessment systems
• Optimize query performance and data availability for experimentation and analytics workflows used by research teams
• Improve the performance, efficiency, and reliability of FAR’s core compute and storage infrastructure, ensuring systems remain fast and stable at scales
• Build highly scalable experimentation and analytics infrastructure to support model evaluation, A/B testing, and feature performance
• Collaborate directly with science and robotics teams to support research projects through both infrastructure development and hands-on technical contribution

Basic Qualifications

• 5+ years of distributed systems experience
• Bachelor's degree in Computer Science or a related field
• Proficiency in Python and at least one systems or backend programming language (e.g., Go, Java, C++)
• Experience with cloud infrastructure platforms (AWS, GCP, or Azure), including compute, storage, and networking services
• Experience building or maintaining data pipelines, ETL systems, or ML training/serving infrastructure
• Understanding of system reliability principles including monitoring, observability, fault tolerance, and on-call operational practices

Preferred Qualifications

• Experience supporting AI/ML research workflows, including building and optimizing training stack, experiment tracking, dataset management, or model deployment infrastructure
• Familiarity with robotics platforms, simulation environments, or real-time systems with strict latency requirements
• Experience with large-scale data processing frameworks (e.g., Apache Spark, Flink, or Ray) and query optimization for analytics workloads
• Demonstrated ability to lead large technical initiatives and influence architectural decisions across cross-functional teams
• Experience building developer tooling, internal platforms, or self-service infrastructure systems that improve research or engineering productivity

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit the Amazon accommodations page for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several U.S. geographic markets. The base pay for this position ranges from $150,000/year in our lowest geographic market up to $300,000/year in our highest geographic market. Pay is based on a number of factors, including market location, and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit the Amazon benefits page. Applicants should apply via our internal or external career site.