Sr. Applied Scientist, Amazon Robotics, Structured Field Coordinated Planning & Control

Amazon•North Reading, MA

About The Position

Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Amazon Robotics is seeking experienced and Senior Applied Scientist with a passion for robotic research. Our team works on challenging and high-impact projects within robotics. Examples of projects include allocating resources to complete a million orders a day, coordinating the motion of thousands of robots and identifying objects and damage. We're the structured field robotics organization powering large-scale mobile robotics operations globally. Our mission is to enable safe, efficient, and reliable robotic operations through intelligent Control, Coordination, and Path Planning systems. We operate at the intersection of planning, algorithmic, and ML research with production systems, owning the full stack from innovation to deployment. Our culture balances research excellence with operational ownership. Applied Scientists partner closely with engineers: reviewing code, contributing to research discussions, and solving problems together. We value deep technical expertise alongside pragmatic engineering judgment. We invest in our people through mentorship and encourage conference participation and knowledge sharing. At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

Requirements

PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 5+ years of applied research experience
Experience programming in Java, C++, Python or related language
Experience with conducting research in a corporate setting
3+ years of industry or academic research experience
3+ years of building machine learning models or developing algorithms for business application experience

Nice To Haves

Have publications on top-tier conferences, such as CVPR, ICCV, ECCV or NeurIPS
Strong foundation in motion planning algorithms (A, RRT, optimization-based planning) and multi-agent coordination
PhD and 6 years of applied research experience

Responsibilities

Lead research initiatives advancing AI-driven structured field robotics (path planning, fleet coordination, control systems) and translate breakthroughs into production solutions at global scale
Own end-to-end delivery of complex algorithmic solutions from design through production deployment and operational maintenance
Drive technical decisions for Control, Coordination, and Path Planning systems meeting performance, scalability, and reliability requirements
Partner with cross-functional teams to translate business requirements into research problems and assess technical risks
Influence technical direction across the broader robotics organization through design reviews and technical discussions with senior engineers and scientists
Demonstrate measurable impact through AI-driven algorithmic improvements: fleet efficiency gains, operational cost reduction, system reliability improvements, and enhanced customer experience
Publish findings at top-tier AI and robotics conferences representing organizational technical leadership
Mentor junior Applied Scientists on research methodology and balancing innovation with production constraints
Operate independently on ambiguous, multi-quarter problems requiring novel AI approaches while navigating tradeoffs between research innovation and production constraints