Software Development Engineer, Infrastructure for Simulation and Science

About the role

The ISS team provides the simulation and ML infrastructure backbone for Amazon Robotics R&D. We build the platforms that science teams use to train robot policies, run large-scale simulations, and bridge the gap between simulated and real-world environments. Our work spans cloud infrastructure (AWS, EKS, Kubernetes), simulation platforms (NVIDIA Isaac Sim), ML tooling (Metaflow, Ray, Weights & Biases), and data systems. We're a collaborative, geographically distributed team that values ownership, operational excellence, and building things that scale. We care about reducing toil, enabling self-service, and making scientists more productive.

Requirements

• 3+ years of non-internship professional software development experience
• 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
• 1+ years of software development engineer or related occupational experience
• 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
• 1+ years of Object Oriented Design experience
• Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
• Experience programming with at least one software programming language
• 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
• Bachelor's degree in computer science or equivalent
• Experience in Kubernetes, Docker or containers ecosystem
• Experience with AWS Services including EC2, Lambda, S3, DynamoDB, SQS
• Experience with ML training infrastructure, distributed computing frameworks (Ray, Spark), or workflow orchestration (Metaflow, Airflow, Step Functions)
• Familiarity with simulation platforms, robotics software, or 3D environments like IsaacSim, IsaacLab, MuJoCo, or Drake
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
• The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers compr

Additional Information

Are you excited about building the infrastructure that powers the next generation of robotics? The Infrastructure for Simulation and Science (ISS) team within Amazon Robotics is looking for a Software Development Engineer II to design and build scalable simulation platforms, ML training pipelines, and data infrastructure that accelerate robotics R&D. You'll work at the intersection of cloud infrastructure, simulation, and machine learning - building systems that enable science teams to train and evaluate robot policies at scale. Your work will directly support multiple robotics programs and have a multiplier effect across the organization. Key job responsibilities -Design, build, and operate scalable simulation infrastructure on AWS (EKS, S3, EC2) that supports robotics R&D workflows. -Develop and maintain ML training pipelines using workflow orchestration tools (Metaflow, Ray, SkyPilot) for distributed compute. -Build data ingestion, streaming, and management systems for large-scale robotics datasets. -Improve platform reliability and reduce operational burden through automation, self-service tooling, and monitoring. -Collaborate with science teams to understand their infrastructure needs and translate them into reusable platform capabilities. -Participate in on-call rotations and operational reviews to maintain high availability of shared infrastructure. -Contribute to architectural decisions, code reviews, and technical documentation. A day in the life You might start the morning reviewing a deployment for a new data pipeline that streams teleoperation data into training workflows. After standup, you pair with a science team member to debug a distributed training job running on GPU clusters. In the afternoon, you work on automating a manual operational process that's been eating into the team's time, then wrap up with a code review for a teammate's infrastructure improvement. Your customers are internal robotics science and software teams, and the problems you solve help them iterate faster on robot learning.

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