Are you motivated by building systems that solve complex, real-world problems at scale? Do you enjoy transforming ambiguous ideas into production systems that drive measurable business impact? At Amazon Robotics, we are a team of builders applying advances in robotics, software, and AI to transform how fulfillment networks operate. Our work directly shapes the efficiency, safety, and reliability of systems used across Amazon’s global operations.

Are you excited about building simulation systems that don’t just model the world—but help design, evaluate, and optimize it in real time? The AR Sortation Insights team develops next-generation platforms that power warehouse innovation across three key areas:
• Simulation – modeling, validating, and optimizing robotic workflows and network designs.
• ML Ops – scalable data pipelines, model training, and inference systems.
• Intelligent Decision Systems – systems that translate insights into actionable outcomes.

This role is primarily focused on the Simulation pillar, where we are reimagining how simulation is built, executed, and consumed. We are evolving from traditional, static simulation workflows to a dynamic, AI-assisted simulation platform that enables faster scenario creation, real-time analysis, and data-driven decision making.

As a Software Development Engineer, you will design and build systems that power:
• Scalable simulation engines and workflows for modeling complex robotic systems.
• Reusable simulation components and configuration frameworks (DSL-driven systems).
• Real-time and post-simulation analytics pipelines to evaluate performance and trade-offs.
• Integration of simulation with ML models and data systems to improve fidelity and accuracy.

You will work at the intersection of distributed systems, data engineering, and applied AI—helping teams rapidly explore design decisions, validate operational strategies, and optimize performance across Amazon’s fulfillment network.

Your work will directly impact how new facilities are designed, how existing systems are optimized, and how quickly we can iterate on ideas—driving measurable improvements in throughput, efficiency, and operational readiness at global scale.


Key job responsibilities
• Design and build scalable simulation systems that model complex robotic workflows, enabling rapid evaluation of design decisions and operational strategies.
• Develop end-to-end simulation pipelines that ingest real-world data, simulate system behavior, and generate actionable insights for planning and optimization.
• Build DSL-driven configuration frameworks and reusable simulation components to reduce development time and enable faster scenario creation and iteration.
• Implement high-performance, event-driven architectures on AWS to support large-scale simulation execution, data processing, and result analysis (both batch and near real-time).
• Integrate machine learning models and data-driven heuristics into simulation environments to improve fidelity, prediction accuracy, and decision quality.
• Develop and maintain data pipelines and simulation data stores, ensuring consistent ingestion, transformation, and accessibility of inputs and outputs across simulation workflows.
• Enable real-time and post-simulation analytics, including KPI evaluation, trade-off analysis, and visualization of system behavior under different scenarios.
• Collaborate closely with operations, solutions design, and engineering teams to translate business problems into simulation models and experiments.
• Conduct large-scale simulation experiments (what-if analysis, A/B comparisons, sensitivity analysis) to validate design choices before production deployment.
• Partner with ML, data engineering, and platform teams to ensure alignment between simulation models, production systems, and real-world performance.
• Build observability and validation frameworks to measure simulation fidelity, track model accuracy, and ensure trust in simulation outputs.
• Own simulation systems end-to-end, including development, deployment, performance optimization, and continuous improvement.
• Contribute to the technical vision and architecture for next-generation simulation platforms, including agentic and AI-assisted simulation workflows.


A day in the life
Day in the Life

Start your day by reviewing results from simulation runs—analyzing throughput, bottlenecks, and system behavior to uncover insights that guide real-world decisions.

Work closely with Solutions Design and Operations to turn ambiguous problems into simulation experiments, defining inputs, assumptions, and success metrics.

Spend your core coding time building scalable simulation systems—from DSL-driven configurations to execution pipelines and data integrations that enable faster scenario creation and iteration.

Run what-if analyses and experiments to validate design choices before they reach production, helping teams move faster with confidence.

Collaborate with engineers, data scientists, and product partners to shape the future of AI-assisted, next-generation simulation platforms.

By the end of the day, your work helps design and optimize robotic systems—impacting how Amazon operates at global scale.

Benefits & Inclusion

Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits vary by location, scheduled hours, length of employment, and job status. For regular, full-time employees, benefits generally include:

Medical, Dental, and Vision Coverage
Maternity and Parental Leave Options
Paid Time Off (PTO)
401(k) Plan

If you’re not sure you meet every qualification listed, we still encourage you to apply. At Amazon, we value diverse backgrounds, experiences, and skillsets. If you’re excited about this role and want to make an impact at global scale, we’d love to hear from you.

About the team
The AR Sortation Insights team builds intelligent platforms that power how Amazon designs, operates, and optimizes its robotic fulfillment network at global scale.

We work across three core areas:

Simulation Platforms – modeling and evaluating complex warehouse workflows to enable faster, data-driven design decisions
Machine Learning Systems – building scalable pipelines for data processing, model training, and real-time inference
Intelligent Decision Systems – developing systems that analyze real-time signals, generate insights, and drive operational improvements

Our mission is to move from static analysis and manual decision-making to dynamic, data-driven systems that continuously learn, adapt, and improve how robotic operations perform.

We partner closely with Solutions Design, Operations, and Engineering teams to solve high-impact problems—ranging from network design and workflow optimization to real-time operational challenges. Our platforms enable teams to simulate, evaluate, and improve systems before and after deployment, reducing risk and accelerating innovation.

As a team, we value:

Ownership – driving solutions end-to-end from idea to production
Customer focus – building tools that directly impact real-world operations
Bias for action – enabling faster iteration and decision-making
Innovation – rethinking how simulation, ML, and intelligent systems come together

Joining this team means working on problems that directly influence how Amazon’s robotic systems are designed and operated—at a scale few organizations operate.